DOCTORAL DEGREE IN AEROSPACE SCIENCE AND TECHNOLOGY

• SUVORKIN, VLADIMIR: Robust positioning using hybridization of GNSS with other measurements of opportunity
  
  Author: SUVORKIN, VLADIMIR
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN AEROSPACE SCIENCE AND TECHNOLOGY
  Department: Department of Physics (FIS)
  Mode: Normal
  Deposit date: 21/04/2026
  Reading date: pending
  Reading time: pending
  Reading place: pending
  Thesis director: ROVIRA GARCIA, ADRIÀ | GONZÁLEZ CASADO, GUILLERMO
  Thesis abstract: Achieving robust positioning across ground and UAV platforms remains challenging under multipath, partial satellite visibility, and rapidly changing measurement quality, especially in urban and embedded scenarios. At the same time, modern smartphones and embedded receivers increasingly provide multi-constellation, dual-frequency observations, carrier-phase measurements, and IMU streams that can be exploited for aided positioning.The present thesis addresses these conditions through a robust GNSS/IMU integration framework, evaluated across heterogeneous sensor grades, from navigation-grade platforms to consumer smartphones. The framework is designed not only for offline post-processing, but also for real-time and embedded operation, following a deterministic execution structure suitable for on-board use.The principal conclusions are:First, a unified processing framework has been developed for GNSS/IMU integration using raw, undifferenced, uncombined GNSS observables (code, carrier phase, Doppler) and inertial measurements.Second, a Square-Root Information Filter (SRIF) architecture has been adopted as the estimator core, enabling a numerically robust implementation and a shared software structure across GNSS-only processing, loosely coupled (LC) fusion, and tightly coupled (TC) fusion modes.Third, Allan deviation analysis has been used systematically to identify inertial noise parameters and to configure the process-noise model of the navigation filter across different IMU classes, improving consistency of tuning when detailed manufacturer specifications are incomplete.Fourth, experimental validation on multiple independent datasets, spanning different GNSS conditions and equipment grades, shows that LC fusion provides the most consistent practical gains, especially in continuity and robustness during short GNSS degradations, and often improves typical solution behaviour when inertial data quality is adequate.Fifth, in dense urban conditions with strong multipath and masking, positioning performance remains fundamentally constrained by GNSS measurement quality and correction level; inertial aiding mitigates short-term disruptions but does not eliminate the GNSS-side error ceiling.Finally, the thesis demonstrates a transferable and numerically stable GNSS/IMU integration framework, a reproducible Allan-based methodology for configuring heterogeneous IMUs in the filter, and a realistic path toward robust positioning under practical field conditions.
  

DOCTORAL DEGREE IN APPLIED MATHEMATICS

• BRUERA MÉNDEZ, RENZO: Stable solutions to some local and nonlocal elliptic problems
  
  Author: BRUERA MÉNDEZ, RENZO
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN APPLIED MATHEMATICS
  Department: School of Mathematics and Statistics (FME)
  Mode: Normal
  Deposit date: 28/04/2026
  Reading date: 22/05/2026
  Reading time: 16:30
  Reading place: Sala d'Actes de l'FME, Edifici U, Campus SudEnllaç videoconferència: https://meet.google.com/faf-egfq-rsq
  Thesis director: CABRE VILAGUT, XAVIER
  Thesis abstract: This thesis concerns stable solutions to some variational elliptic partial differential and integro-differential equations. Stable solutions are critical points of the energy functional associated to the equation at which the second variation of the energy is nonnegative. That is to say, essentially, they are local minimizers of the energy functional. As such, stable solutions usually constitute the observed state of many physical systems. From a mathematical perspective, they enjoy interesting properties that are not shared by general solutions.The thesis is divided into two parts.In the first part, we address the issue of the regularity of stable solutions to a class of semilinear elliptic reaction-diffusion equations with singular nonlinearities (i.e., nonlinearities that are unbounded). They are usually called MEMS-type equations. Our main contribution is to show that stable solutions to such problems are smooth up to the optimal dimension n = 6 whenever the nonlinearity f satisfies a convexity assumption. Moreover, we prove, for the first time in the literature, interior a priori estimates for solutions to these problems that hold regardless of the boundary condition satisfied by the solution. We also give global estimates for the Dirichlet problem with vanishing boundary condition.In the second part, we first study the existence of periodic constant nonlocal anisotropic mean curvature surfaces of revolution, which we call anisotropic nonlocal Delaunay surfaces. We show that such surfaces arise as minimizers of a periodic version of the anisotropic nonlocal perimeter, and we also construct them as perturbations of straight cylinders in the plane—we call these near-cylinders. Finally, in the last chapter, we characterize the stability of these near-cylinders in the homogeneous and isotropic case, and we show that the only minimizers in R2 of the periodic nonlocal perimeter that are close to straight cylinders are, possibly, straight cylinders themselves.
  

DOCTORAL DEGREE IN ARTIFICIAL INTELLIGENCE

• HERNÀNDEZ CARNERERO, ÀLVAR: Generalizable Time Series Classification Models for Complex Real-World Applications
  
  Author: HERNÀNDEZ CARNERERO, ÀLVAR
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN ARTIFICIAL INTELLIGENCE
  Department: Department of Computer Science (CS)
  Mode: Normal
  Deposit date: 23/04/2026
  Reading date: 08/06/2026
  Reading time: 12:00
  Reading place: Sala d'actes de la FIB - Edifici B6 planta 0, Campus Nord
  Thesis director: SANCHEZ MARRE, MIQUEL | VAZQUEZ SALCEDA, JAVIER
  Thesis abstract: This thesis develops a comprehensive framework for Time Series Classification (TSC) focused on real-world applications characterized by limited data availability, heterogeneous datasets, and complex temporal structure, which concludes with a generalizable and adaptive modelling approach. This research conducts a comprehensive survey of state-of-the-art methods, identifying their strengths and limitations, with a focus on specific domains such as antimicrobial prediction, astronomy, and physiological monitoring. Building on these insights, preprocessing pipelines are developed for the considered time series, including new temporally informed feature generation grounded in domain knowledge, robust feature importance estimation, and wrapper-based feature selection. Methodologically, windowing strategies are engineered to mitigate data scarcity and temporal distribution shifts, a hybrid modular architecture is proposed to integrate mechanisms suited to the specific needs of the data, and an adaptation of additive attention is formulated. The methodology employs ensembles to enhance predictive stability and evaluates model interpretability by analyzing how the trained models prioritize features and by visualizing attention patterns across the sequence to reveal temporal relevance patterns.A central contribution of the thesis is CLAIM (Content-modulated Low-rank Adaptive Isotropic Mixer), a novel architecture for TSC that synthesizes insights from our research and from recent advances in sequence modelling. CLAIM combines content-modulated dynamic mixing, adaptive inductive biases, and a low-rank isotropic block design, resulting in an efficient and interpretable model. Experiments across heterogeneous datasets (including intensive care unit patient trajectories, exoplanet transit signals, and electrocardiogram waveforms) show that CLAIM frequently achieves the best performance in small-data regimes compared to temporal transformer and Multilayer Perceptron Mixer (MLP-Mixer) baselines. CLAIM also offers practical advantages in interpretability, due to its dynamic weights and explicit inductive-bias components. In addition, it demonstrates strong computational efficiency, particularly at inference time. The model modulates structural priors such as locality, causality, and location invariance in a data-driven manner, aligning with effective contemporary design principles for temporal modelling. Furthermore, it adapts its focus across domains, as reflected in the dynamic weighting of temporal components, which corresponds with established domain knowledge.The thesis contributes a unified perspective on real-world TSC, pairing domain-guided pipelines with a generalizable modelling approach. The resulting CLAIM architecture offers an interpretable, efficient, and adaptive solution that advances the state of the art in small-data TSC.
  
• OLIVA FELIPE, LUIS JAVIER: Artificial Virtuous Agents in water management scenarios
  
  Author: OLIVA FELIPE, LUIS JAVIER
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN ARTIFICIAL INTELLIGENCE
  Department: Department of Computer Science (CS)
  Mode: Normal
  Deposit date: 24/03/2026
  Reading date: 22/05/2026
  Reading time: 11:00
  Reading place: FIB Sala d'actes Manuel Martí Recober B6-planta 0Campus Nord
  Thesis director: CORTÉS GARCÍA, CLAUDIO ULISES | CORTÉS MARTÍNEZ, ATIA
  Thesis abstract: As human populations continue to grow, resource consumption rises inexorably, leading to eventual depletion, as stated by Malthus and especially by Hardin's Tragedy of the commons. To maintain resource sustainability, coordinating the collective action of those who seek benefit from consuming that resource is necessary. However, there is a small part of the whole set of participants which may act selfishly and ignore any norm or social convention that constrains overconsumption or regulates the way it is consumed. This occurs because collective action, as explained by Olson in Logics of Collective Action (Olson, 1971), can face two different social dilemmas: The prisoner's dilemma and the free-rider dilemma. The former explains how individual choice leads to a local optimal (i.e., personal benefit) but at the cost of a bad global result (i.e., common good gets depleted and, finally, nobody benefits any longer). The latter explains how free-riders, i.e., selfish or rogue agents, can burden the maintenance of the common good as the group of consumers grow since avoiding paying the cost to preserve it may not be perceived (e.g., reducing the consumption rate or paying taxes) while the common good is still provided and consumers cannot be excluded.This thesis explores the use of virtue ethics in the decision-making process of agents. Virtues are usually associated with moral excellence or a conformity to a standard of right. In our case, the goal is to represent, to some extent, virtuosity according to classic ethics, concretely, Plato’s and Aristotle’s ethical schools of thought. In Plato and Aristotle's writings, virtues are usually defined as character traits that drive how an agent should behave in the world. In both approaches the goal of the agent is to act in the right way, according to virtues, to achieve a state of eudaimonia (having a plentiful and fulfilling life). We understand that the rightness of an agent is not only determined by what they want to do but also by how they balance their actions to perform them (i.e., acting according to your values and virtues). We would like to analyse how to model values and virtues to represent, to some extend, a Platonic and Eudaimonistic form of virtue ethics. Our model of values and virtues uses Swanton’s target-centered approach to virtue ethics.In both ethical approaches, it is not only necessary to consider the individual but also the society these individuals live within. These societies should foster their individuals to be virtuous. Since values and virtues are considered incommensurable, we decided to focus on a specific context to which is water consumption. For this reason, the thesis is divided into two main chapters: one focusing on the individual consumption of fresh water in households in a mid-size Mediterranean town, and another at the societal level, which uses a model of industrial wastewater management in the besos river basin, whose catchment is located in Barcelona, and is a typical example of a Mediterranean complex catchment. The catchment area is one of the most populated catchments in Catalonia, having more than 2M people connected, whose working population is mainly occupied in industrial activities and the services sector. These industries are of various activities with an important presence of the chemical, metallurgical, plastics, textile, paper and food sectors. Each of these sectors has different needs with regard to water usage.We raise the hypothesis that artificial virtue agents may perform better than classic utilitarian agents with regards to common-pool resource sustainable consumption. We think that both approaches (Platonic and Aristotelian) are not better to each other but depending on the context it is possible that significant differences become relevant. That is, both are two different ways to act rightfully.
  
• VILALTA ARIAS, ARMAND: Semantic Embeddings in Deep Convolutional Neural Networks
  
  Author: VILALTA ARIAS, ARMAND
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN ARTIFICIAL INTELLIGENCE
  Department: Department of Computer Science (CS)
  Mode: Normal
  Deposit date: 26/03/2026
  Reading date: 15/06/2026
  Reading time: 11:00
  Reading place: Sala d'actes de la FIB - Edifici B6, Campus Nord Barcelona
  Thesis director: GARCÍA GASULLA, DARIO | CORTÉS GARCÍA, CLAUDIO ULISES
  Thesis abstract: One of the fundamental questions in Artificial Intelligence (AI) is how to represent knowledge effectively. The main challenge lies in devising a representation that can be exploited for intelligent computations. Since 2012, when Deep Neural Networks (DNNs) established themselves as the state of the art in Computer Vision, their use has rapidly expanded to a wide range of AI domains, including text and speech recognition, image generation, gaming, and robotics. This widespread success stems from their nature as representation learning techniques. However, DNNs require large volumes of data and considerable computational resources to learn such representations, which significantly limits the range of problems to which these models can be directly applied.In this thesis, we explore the possibility of reusing the knowledge learned by a DNN for a specific problem to solve a different one, which is commonly known as transfer learning. We focus on the representations learned by a DNN, codified in the activations of its neurons in response to an input, namely embeddings. All this work stems from a first analysis to understand where valuable information is encoded in Convolutional Neural Networks (CNNs) within the context of transfer learning. When used for characterizing every class of eleven datasets, we statistically measure the discriminative power of every feature found within a deep CNN. We seek to provide new insights into the behaviour of CNN features, particularly the ones from convolutional layers, which they had not used in previous literature. Our results confirm that low and middle level features may behave differently from high-level features, but only under certain conditions. We find that all CNN features can be used for knowledge representation purposes both by their presence or by their absence, doubling the information a single CNN feature may provide. We also study how much noise these features may include and propose a thresholding approach to discard most of it.This enables the definition of a methodology to improve the generalization capabilities of CNN representations, which we refer to as the Full-Network Embedding (FNE), which successfully integrates convolutional and fully connected features. To do so, the embedding normalizes features in the context of the problem and discretizes their values to reduce noise and regularize the embedding space. Significantly, this also reduces the computational cost of processing the resultant representations. The proposed method outperforms single layer embeddings on several image classification tasks while also being more robust to the choice of the pre-trained model used for obtaining the initial features. The performance gap in classification accuracy between thoroughly tuned solutions and the full-network embedding is also reduced, making the proposed approach a competitive solution for a large set of applications. We consider FNE more semantic as it understands CNN activations as semantic concepts. Similarly to many human languages, it considers if a feature of the input is significant in a given context and if it is because of its presence or absence.A first stem is the use of the FNE as the basis for a network representation of concepts where complex network techniques are applied. We propose the construction of a graph embedding space, introducing a methodology to transform the knowledge coded within a deep convolutional network into a topological space.The second stem, uses the same techniques in the context of multimodal embeddings representing text and images. The FNE provides a multi-scale representation of images, which results in richer characterizations while focusing on the relevant information through its semantic discretization. Results for image annotation and image retrieval tasks show a constant improvement when applied to different existing methodologies.
  

DOCTORAL DEGREE IN AUTOMATIC CONTROL, ROBOTICS AND VISION

• PÉREZ QUINTANA, MARC: Multimodal Data Fusion for Multiple Object Tracking: A Reference Perception System for ADAS and AV Functions Validation
  
  Author: PÉREZ QUINTANA, MARC
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN AUTOMATIC CONTROL, ROBOTICS AND VISION
  Department: Institute of Robotics and Industrial Informatics (IRI)
  Mode: Normal
  Deposit date: 21/04/2026
  Reading date: 02/06/2026
  Reading time: 11:00
  Reading place: Sala d'Actes de la Facultat de Matemàtiques i Estadística (FME), Campus Diagonal Sud, Carrer de Pau Gargallo, 14, 08028 Barcelona
  Thesis director: AGUDO MARTÍNEZ, ANTONIO
  Thesis abstract: In 2021, 1.19 million people died from traffic accidents, which are the leading cause of death among people aged 5 to 29 years. Most of these accidents are caused by the driver; therefore, automated vehicles present a unique opportunity to reduce fatalities and improve road safety. But the large-scale deployment and adoption of safe automated vehicles require a robust validation procedure, including open-road tests, which are significantly more challenging than tests on proving grounds or in simulation, because there is no ground-truth data on the objects around the vehicle under test. This thesis studies how to build a reference perception system to enable the validation of automated vehicles and advanced driver assistance systems on the open road. We discuss object detection and present a clustering method to detect class-agnostic objects on point clouds. These detections can be projected to the image plane to be combined with image-based detections to improve robustness and add class information. We also present a method to exploit class prototypes, defined as the mean and covariance of the features of that class, to improve the performance of a learning-based object detector on point clouds. Object detections from different modalities can be combined in the presented multiple object tracking method, which works without assuming sensor synchronization and can be adapted to include object information from any source, such as vehicle-to-vehicle communications, and includes the handling of common errors that are understudied in the literature: Misclassifications and partial bounding box detections. These object detectors and multiple object trackers can be evaluated with a novel proposed methodology to evaluate perception systems focusing on safety, which we use to evaluate the effect on object detection performance of different weather conditions and the robustness layers included in recent testing protocols. We also present a pipeline to extract scenarios in standardized formats from recorded images and point clouds, that can be directly used by a simulator. Finally, we discuss how scenario extraction, combined with other tools, can help in the validation of automated vehicles on the open road.
  

DOCTORAL DEGREE IN BUSINESS ADMINISTRATION AND MANAGEMENT

• GAMARRA GAMARRA, MARÍA DEL PILAR: Comportamiento Ético: Identidad Moral, Atención Moral y su papel en la Construcción del Liderazgo Ético
  
  Author: GAMARRA GAMARRA, MARÍA DEL PILAR
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN BUSINESS ADMINISTRATION AND MANAGEMENT
  Department: Department of Management (OE)
  Mode: Normal
  Deposit date: 30/04/2026
  Reading date: pending
  Reading time: pending
  Reading place: pending
  Thesis director: OLIVELLA NADAL, JORGE | GIROTTO, MICHELE
  Thesis abstract: When a leader acts without ethics, the entire organization is shaken; when leadership is exercised with integrity, it inspires sustainable cultures and strengthens social trust. This doctoral thesis addresses the challenge of understanding how leaders perceive and enact their own ethical behavior, focusing on two fundamental internal drivers: moral identity and moral attentiveness. These factors, often invisible in everyday organizational discourse, distinguish formal leadership from authentic leadership capable of generating legitimacy, resilience, and commitment in complex contexts.The research is grounded in a clear diagnosis: financial scandals, corporate misconduct, and growing social demands for transparency have placed ethics at the center of the business agenda. However, most previous studies have examined ethical leadership from the perspective of subordinates or within Anglo-Saxon contexts, leaving aside the leader’s own viewpoint and the analysis of specific cultural realities such as the Spanish context. This is the gap that the thesis seeks to address.The study is developed in two phases. The first presents a bibliometric analysis and a systematic review of more than thirty years of scientific literature, identifying three theoretical pillars of ethical leadership: values-based theories, cognitive moral development theory, and social learning theory. This review highlights the fragmentation of existing approaches and the scarcity of empirical studies focused on leaders’ self-perceptions, as well as the need to incorporate variables such as gender, age, and hierarchical level.The second phase builds an empirical model applied to the Spanish context. To this end, the classic ethical leadership scale is psychometrically validated and adapted to leaders’ self-perceptions through exploratory and confirmatory factor analyses, reliability tests, and structural equation modeling. The process includes expert review, pilot testing, and the administration of questionnaires to a sample of managers from different sectors. The result is a robust, context-adapted scale that identifies three dimensions of self-perceived ethical leadership: integrative decision-making, the reinforcement of ethical behaviors, and the leader’s personal example as a role model.The findings show that moral identity and moral attentiveness significantly influence these dimensions, albeit in different ways. The internalization of moral identity predicts the integration of ethical principles into decision-making, while the symbolization of moral identity and reflective moral attentiveness explain the tendency to reinforce ethical behaviors. In addition, variables such as gender, age, managerial experience, and hierarchical position act as moderators, indicating that ethical leadership emerges through the interaction of personal and professional characteristics.Overall, this thesis demonstrates that ethical leadership is not an abstract code or a superficial discourse, but a practice rooted in the leader’s internal coherence, reflected in decision-making, and projected onto organizational culture, contributing to the development of more just and sustainable organizations.
  

DOCTORAL DEGREE IN CIVIL ENGINEERING

• DAR, ZULKEEFAL: Artificial Neural Networks Augmented Reduced Order Models for Fluid-Based Optimization Problems
  
  Author: DAR, ZULKEEFAL
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN CIVIL ENGINEERING
  Department: Barcelona School of Civil Engineering (ETSECCPB)
  Mode: Normal
  Deposit date: 14/04/2026
  Reading date: 18/05/2026
  Reading time: 12:00
  Reading place: UPC Campus Nord, ETSECCPB, C/ Jordi Girona 1-3, edificio C1, Sala 002, Barcelona
  Thesis director: CODINA ROVIRA, RAMON | BAIGES AZNAR, JOAN
  Thesis abstract: This work presents an artificial neural network (ANN) augmented reduced order modeling framework for transient fluid-based optimization problems. The methodology builds upon a projection-based reduced order model (PROM) derived from a variational multiscale stabilized finite element formulation. While PROMs offer significant computational savings, their predictive capability deteriorates in strongly nonlinear regimes and under parameter variations, particularly when employed within transient optimization loops. To ensure computational efficiency during the repeated evaluations required by the optimization procedure and transient nature of the problem, hyper-reduction is incorporated into the reduced framework. Although this further decreases computational cost, it introduces an additional approximation, which may amplify the loss of accuracy already caused by modal truncation.To address these limitations, a data-driven correction strategy is introduced in which an ANN is trained to approximate the error in the fully discrete reduced system. Rather than replacing the reduced model or directly correcting the solution, the ANN is embedded as an additive correction term in the fully discrete reduced equations. In this sense, it acts as a closure model that compensates for the combined effects of modal truncation and hyper-reduction. The correction restores consistency between the reduced evolution and the underlying high-fidelity solution while preserving the original Galerkin projection structure.The proposed framework is assessed in the context of aerodynamic shape optimization at low Reynolds number and high angle of attack, where nonlinear flow features challengestandard reduced models. The optimization is carried out using a gradient-based strategy, and the ANN-augmented reduced model is used as a surrogate to accelerate the objectivefunction evaluation within the optimization loop. Results demonstrate that the proposed approach significantly improves predictive accuracy compared to the classical projection-based reduced model, while retaining substantial computational savings with respect to the full order model.
  
• LOPEZ CHACON, SERGIO RICARDO: Enhanced short-term prediction of high streamflow combining physically based and machine learning models
  
  Author: LOPEZ CHACON, SERGIO RICARDO
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN CIVIL ENGINEERING
  Department: Barcelona School of Civil Engineering (ETSECCPB)
  Mode: Article-based thesis
  Deposit date: 21/04/2026
  Reading date: 11/06/2026
  Reading time: 12:30
  Reading place: UPC Campus Nord, ETSECCPB, C/ Jordi Girona 1-3, edificio C1, Sala 002, Barcelona
  Thesis director: BLADE CASTELLET, ERNEST | SALAZAR GONZÁLEZ, FERNANDO
  Thesis abstract: Machine learning models have demonstrated a strong potential in the recent decade for streamflow prediction purposes. Even reaching considerable accuracy, machine learning models still present some limitations and little explore aspects on this topic: the accuracy decrease on high streamflow, uncertainty estimation applications, and hydrological interpretation of models. High streamflow values are the most relevant for early warning systems of flood mitigation. However, these records are scarce in the data. Hence, a decrease in accuracy is seen, which gets deeper in extrapolated scenarios. This thesis proposes two methodologies that support machine learning models with the outputs of a physically based model to enhance the prediction capabilities in high streamflow prediction. The first methodology produces a machine learning model trained with a combination of observed and synthetic high streamflow events generated by a physically based model. The second methodology creates a hybrid model that combines outputs based on a physically based model and the prediction of its residuals by a machine learning model. Both methodologies have shown accuracy improvements compared to models trained only with observed data, reaching reductions of root mean square errors of more than 23% for streamflow values larger than the 3-year return period in the study area. However, the second methodology reaches closer approximations to the peak observed value with significant extrapolation capabilities. Despite the accuracy, the uncertainty related to the model is a main topic to explore. A methodology to estimate the uncertainty of the output of a streamflow prediction model by employing machine learning techniques is developed in the thesis. The results of the methodology show that the distributions of the residuals can be acceptably described. Consequently, the uncertainty interval covers 87.9% of the observed values higher than the 3-year return period with a width of interval significantly smaller than the broadly used Box-Cox method. Finally, the hydrological interpretation of a machine learning model for streamflow prediction is undertaken. The results show that the model may identify areas of the catchment whose runoff considerably contributes to the control point, as well as period of previous soil saturation, and the impact of the highest precipitation values in the model’s prediction. When the hydrograph rises steeply, the model acceptably considers the recent accumulated precipitation as the most relevant features along with previous saturation. As the hydrograph peak approaches and during the falling limb, previous streamflow acquires main relevance supported by runoff of distant regions. The machine learning model can suitably interpret the catchment system and provide valuable information.
  

DOCTORAL DEGREE IN COMPUTATIONAL AND APPLIED PHYSICS

• JURADO ROMERO, ARNAU: Self-Propulsion of Molecular Swimmers
  
  Author: JURADO ROMERO, ARNAU
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN COMPUTATIONAL AND APPLIED PHYSICS
  Department: Department of Physics (FIS)
  Mode: Normal
  Deposit date: 28/04/2026
  Reading date: 28/05/2026
  Reading time: 11:00
  Reading place: Sala de Juntes de la FIB
  Thesis director: REY ORIOL, ROSENDO | CALERO BORRALLO, CARLOS
  Thesis abstract: Active matter systems have the ability to exhibit self-propulsion by consuming energy to produce mechanical work, staying out of equilibrium. They occur on a vast range of scales, from herding mammals and flocking birds down to bacteria. Understanding these systems requires the study of the mechanisms that drive the system out of equilibrium and the emergent collective phenomena that result from activity. Both these questions require a multidisciplinary approach, the innovative application of classical non-equilibrium physics, as well as the development of new theoretical frameworks.A major goal in the field is the development of artificial micro- and nano-scale particles capable of self-propulsion, so-called swimmers. These swimmers hold the potential for groundbreaking applications, such as targeted drug delivery, non-invasive microsurgery, and water purification.This overarching technological goal is met with a number of theoretical and practical challenges, of which the search for viable propulsion mechanisms at the nano-scale is only one of them. However, achieving propulsion at such small scales is limited by the hydrodynamic regime characteristic of these scales, in which reciprocal deformations cannot produce net propulsion. In addition, rotational diffusion, which severely hinders the self-propelling capabilities of a swimmer, increases rapidly with diminishing size.This thesis demonstrates a propulsion mechanism for a small molecule, nitromethane, via all-atom molecular dynamics simulations. The molecule is subject to a high energy vibrational excitation which is then released anisotropically onto the surrounding water solvent. This results in propulsion velocity bursts that are able to enhance the translational diffusion of the molecule. Making use of the all-atom nature of the simulations, the propulsion is explained mechanistically and in terms of self-thermophoresis, an ubiquitous mechanism in micro-metric self-propelling colloids. The nitromethane model thus constitutes the smallest example of a self-propelling particle.Motivated by these findings, the dynamics of self-propelled particles under periodic time dependent propulsion velocities are investigated. By extending the popular Active Brownian Particle (ABP) model, we obtain the translational diffusion enhancement of exponentially decaying propulsion, similar to the one exhibited by nitromethane. The results show that narrow, intense, peaks of activity, sufficiently spaced in time, are able to greatly enhance the diffusion of the swimmer. This strategy provides new avenues for mitigating the negative effect of rotational diffusion as swimmers are downsized.The collective dynamics of a many-body system of self-propelled particles under exponentially decaying periodic activity are also investigated. A well-known collective phenomenon in active matter is the emergence of Motility Induced Phase Separation (MIPS), consisting in the activity-driven phase separation into dense and dilute phases. The inclusion of time modulation in the system results in a significant alteration of the MIPS phase diagram, which has been thoroughly characterized. In some cases, phase separation is severely suppressed, allowing the system to remain as a single homogeneous phase while maintaining high mobility particles.Finally, a class of state-of-the-art machine learning interatomic potentials, based on neural networks, is presented and utilized for the study of thermophoresis, a phenomenon closely related to the propulsion mechanism of nitromethane and with a marked sensitivity to solute-solvent interactions. Neural network potentials allow the exploration of large scale systems with precision rivaling first-principles quantum calculations. These new algorithms are being developed at a fast pace and allow for the accurate characterization of condensed matter systems, a critical ingredient in the development of functionalized self-propelling particles.
  

DOCTORAL DEGREE IN EARTHQUAKE ENGINEERING AND STRUCTURAL DYNAMICS

• NAVARRO GRANADOS, JORDI: Theoretical and Experimental Study on Cold-Formed Elements for Steel Framing in Seismic Areas
  
  Author: NAVARRO GRANADOS, JORDI
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN EARTHQUAKE ENGINEERING AND STRUCTURAL DYNAMICS
  Department: Department of Civil and Environmental Engineering (DECA)
  Mode: Normal
  Deposit date: 20/03/2026
  Reading date: 29/05/2026
  Reading time: 12:30
  Reading place: ETSEIBUPC, Campus SudAula CapellaAv. Diagonal, 647.08028 Barcelona
  Thesis director: CASAFONT RIBERA, MIQUEL | BOVÉ TOUS, ORIOL
  Thesis abstract: This Thesis presents a theoretical and experimental study on cold-formed elements for seismic-resistant steel framing systems. The focus is on evaluating the feasibility of using low- and mid-rise buildings based on cold-formed steel framing with flat strap bracings as diagonal members, and built-up steel profiles or concrete-filled steel sections as boundary (chord) studs, to resist seismic actions in moderate to high seismicity areas. Three representative prototype residential buildings with five, seven, and ten storeys, located in areas with peak ground accelerations (PGA) of 0.2 g, 0.3 g, and 0.4 g have been considered.This Thesis consists of six chapters; the first is an introduction, the second describes the current state of the art, and the last presents conclusions and future research. Chapters 3, 4, and 5 constitute the main body of this Thesis, and their content is described below.Chapter 3 presents a theoretical investigation into the compressive performance of single Cold-Formed Steel (CFS) members with Cee-shaped cross-sections, as well as various built-up sections (back-to-back, toe-to-toe, nested and a stud pack 4) derived from Cee profiles. The initial assessment of compressive performance is conducted using closed-form hand expressions. These results are subsequently complemented by design based on the Finite Strip Method (FSM) and further refined through simulations employing the Generalised Beam Theory (GBT). Detailed results for the compressive performance of the studied sections, considering a range of parameters including section thicknesses, steel grades, and buckling lengths considered, are also provided. Chapter 4 describes the performed experimental investigation into the compressive performance of steel-only and concrete-filled cold-formed steel built-up sections. The study encompasses the design and preparation of the test specimens, the experimental results, the predicted compressive resistance, and a comparative analysis between the predicted and observed values. Particular attention is given to the increasing of the effective area resulting from concrete infill; which is theoretically and experimentally validated through modifications to existing design approaches. Additionally, Chapter 4 analyses the compressive resistance of two CFS built-up sections filled with concrete -referred to as 4-pack Concrete-Filled Cold-Formed Steel (CF CFS) and 6-pack Concrete-Filled Cold-Formed Steel (CF CFS)-, across a range of concrete strength classes, from C20/25 to C50/60.Chapter 5 develops the design of three representative prototype steel framing buildings, 5-, 7-, and 10-storey, in regions of moderate (PGA 0.2 g) and high seismicity (PGA 0.3 g and 0.4 g) areas, by means of a Finite Element Method software, complemented with hand calculations. The main goal is obtained the internal forces in the most critical members of the buildings (namely the chord studs and the diagonal bracings of the wall panels). As described in detail in Chapter 5, the spectral ordinates, the base shear forces, and their distribution along the height of the buildings have been determined by hand calculations in accordance with the provisions of the second-generation Eurocode, since these provisions are not yet implemented in the FEM software. The results of the seismic analyses for each prototype building and seismicity level are presented, as well as a sizing proposal of the chord studs based on both the internal forces and the compressive performance of the concrete-filled in Chapters 3 and 4.
  

DOCTORAL DEGREE IN ELECTRONIC ENGINEERING

• VILELLA VEGA, MANEL: Enhanced modulation strategies for a high-efficiency energy conversion system for electric vehicles
  
  Author: VILELLA VEGA, MANEL
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN ELECTRONIC ENGINEERING
  Department: Department of Electronic Engineering (EEL)
  Mode: Normal
  Deposit date: 22/04/2026
  Reading date: pending
  Reading time: pending
  Reading place: pending
  Thesis director: ZARAGOZA BERTOMEU, JORDI | BERBEL ARTAL, NÉSTOR
  Thesis abstract: The global transition toward electric vehicles (EVs) has intensified the demand for high-efficiency power electronics. On-Board chargers (OBCs) and bidirectional energy conversion systems are essential to manage the flow between the grid and the vehicle battery. These systems must be compact, lightweight, and capable of high-power density to satisfy modern automotive requirements. In this context, isolated bidirectional DC-DC conversion, specifically the Dual Active Bridge (DAB) converter, has emerged as a key technology due to its inherent galvanic isolation and high performance. To achieve high power density and efficiency, this research utilizes WBG semiconductors, specifically GaN devices, which allow for higher switching frequencies compared to traditional silicon.The primary objective of this doctoral work is to optimise control and modulation techniques to exploit the full potential of GaN semiconductors while mitigating high-frequency operation challenges, such as parasitic effects, thermal management, and EMI. The research is structured around four main contributions.First, the thesis experimentally evaluates a GaN-based non-resonant DAB converter, analysing the trade-offs between switching frequency, efficiency, and EMI. It demonstrates that increasing the switching frequency up to 200 kHz maintains high efficiency while enabling a significant reduction in the size of passive components without compromising electromagnetic compatibility.Secondly, a mathematical quantification of the steady-state DC current in non-resonant DAB converters is introduced. The formulation, which accounts for semiconductor resistance variations and timing mismatches, is validated experimentally and coupled with a closed-loop voltage compensation control strategy to effectively mitigate these parasitic DC currents.Thirdly, a variable frequency modulation is developed to reduce transient surge currents during start-up and load variations. Finally, the thesis explores the modular series-parallel connection of converters to scale the system for different EV battery voltages.
  

DOCTORAL DEGREE IN ENVIRONMENTAL ENGINEERING

• HOUCHMAND, LAURA JO: Integrated assessment of passive rooftop strategies and photovoltaic on building energy demand and urban heat island effects under current and future Mediterranean climates
  
  Author: HOUCHMAND, LAURA JO
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN ENVIRONMENTAL ENGINEERING
  Department: Department of Civil and Environmental Engineering (DECA)
  Mode: Normal
  Deposit date: 08/04/2026
  Reading date: 02/06/2026
  Reading time: 12:00
  Reading place: UPC ESEIAATTerrassa, TR5, Aula 2.0a 226Carrer de Colom, 15, 08222 Terrassa, Barcelona
  Thesis director: GASSO DOMINGO, SANTIAGO | MACARULLA MARTÍ, MARCEL
  Thesis abstract: Climate change mitigation and adaptation are pressing challenges, particular in Mediterranean cities, where rising temperatures, intensifying urban heat island (UHI) effects, increasing cooling demand, and growing water scarcity intersect. Rooftops represent a critical interface for climate-responsive building transformation, as they offer substantial potential for passive strategies, such as cool roofs and green roofs, and active renewable technologies, particularly photovoltaic (PV) systems. Despite extensive research on rooftop technologies, four key knowledge gaps remain: (1) insufficient year-round assessment of the UHI impact of roof-mounted PV systems combined with passive roofing strategies; (2) limited integrated evaluation of green roofs and PV systems considering both building energy demand and urban climate implications; (3) lack of comparative analysis of the impact of passive roofing strategies under projected future Mediterranean climate scenarios on the buildings’ energy demand; and (4) inadequate quantification of the impacts of PV integration with passive roofing strategies on building energy demand under future climate change conditions. This doctoral research addresses these gaps through a structured, simulation-based methodological framework. The work begins with a targeted literature review to establish the scientific context of passive and active rooftop strategies in Mediterranean climates and to define the research questions. Building on these gaps, dynamic building energy simulations are conducted using DesignBuilder with EnergyPlus as the calculation engine. Barcelona (Csa climate classification) serves as the case study location. Current climate conditions are represented by high-resolution Typical Meteorological Year (TMY) data (1975–2021), while future conditions are assessed using morphed TMY files based on the IEA EBC Annex 80 methodology under RCP4.5 and RCP8.5 scenarios for mid-term (2047–2060) and long-term (2087–2100) horizons.A typical Mediterranean roof as basic roof (BR) serves as the reference case and is compared with passive strategies, including a cool roof (CR), a soil roof without vegetation (SR), and extensive green roofs under different irrigation regimes (EGR, EGRmin, EGRmax), as well as their integration with rooftop PV systems. Key performance indicators include convective heat fluxes from roof and PV surfaces (UHI contribution), conductive heat fluxes through the roof and annual heating and cooling demand (building energy demand), as well as PV electricity generation.The findings of this thesis highlight trade-offs between energy efficiency, urban heat, water use, and future climate impacts, emphasizing the need for climate-sensitive, typology-specific rooftop strategies.
  

DOCTORAL DEGREE IN MARINE SCIENCES

• PAVO FERNÁNDEZ, EVA: Coastal dynamics and resilience in the low-engineered coast of Mozambique
  
  Author: PAVO FERNÁNDEZ, EVA
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN MARINE SCIENCES
  Department: Department of Civil and Environmental Engineering (DECA)
  Mode: Normal
  Deposit date: 23/03/2026
  Reading date: 22/05/2026
  Reading time: 11:30
  Reading place: ETSETB Sala TeleensenyamentEdifici B3 - Ricardo Valle Sala 103 Planta 1Campus Nord Jordi Girona, 1-308034 Barcelona
  Thesis director: GRACIA GARCIA, VICENTE | GRIFOLL COLLS, MANUEL
  Thesis abstract: Coastal areas are crucial and sensitive environments that are increasingly at risk due to climate change. Yet, a significant research gap persists in integrating the various scales of coastal analysis, from long-term geomorphological settings to short-term (extreme events), in regions with limited information, as is the case of Mozambique.This thesis tackles this gap by utilizing a comprehensive, multiscale approach to study the dynamic and under-researched coastline of Mozambique. The region serves as a compelling case study, presenting a diverse geomorphology and high vulnerability to tropical cyclones within a data-scarce context. The research uses high-resolution satellite imagery, hydrodynamic data, geomorphological mapping, and process-based numerical modelling to provide a holistic assessment of coastal processes.By bridging the scales between remote sensing, geomorphological context, and process-based modelling, this thesis aims to advance the predictive understanding of coastal change in Mozambique. The outcomes provide a scientific foundation for informed coastal management, support coastal resilience, and offer a transferable template for studying other vulnerable, data-scarce coastlines globally.
  
• SÁNCHEZ-ARCILLA ROSANAS, AGUSTÍN: PREDICTIVE MAINTENANCE OF HARBOUR STRUCTURES AND COASTAL SYSTEMS
  
  Author: SÁNCHEZ-ARCILLA ROSANAS, AGUSTÍN
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN MARINE SCIENCES
  Department: Department of Civil and Environmental Engineering (DECA)
  Mode: Article-based thesis
  Deposit date: 26/03/2026
  Reading date: 22/05/2026
  Reading time: 11:30
  Reading place: Place: ETSECCPBUPC, Campus NordBuilding C1. Classroom: 002C/Jordi Girona, 1-308034 Barcelona
  Thesis director: SANCHEZ-ARCILLA CONEJO, AGUSTIN | ALTOMARE, CORRADO
  Thesis abstract: This PhD thesis contributes to the understanding and maintenance of Mediterranean and Atlantic coastal systems under accelerating climatic and human pressures. For that purpose a new methodology has been developed, combining beach morphodynamics, adaptation pathways with tipping points, and considering relevant institutional barriers to coastal restoration. The developed methodology focuses on combining process-based coastal engineering, machine learning techniques and adaptation governance frameworks to get a systemic approach for an adaptation based on restoration.Such an approach considers the barriers to coastal restoration, along with the necessary “enablers” to overcome them, always supported by coastal engineering techniques, process-based models, predictions using machine learning, and adaptation governance frameworks. In this way, a systemic approach to coastal dynamics is obtained, making it possible to make decisions based on predicting and analyzing how a typical beach may evolve under future climate scenarios and what the most appropriate responses are.Mediterranean beaches are highly dynamic albeit constrained systems often with scarcity of sediments, encroachment of urban areas, and limited accommodation space. These factors, together with growing human pressures, are expected to increase the risk of erosion and reduce the resilience of the considered beaches. The first contribution of this thesis is developed in the paper “Coastal Adaptation Pathways and Tipping Points for Typical Mediterranean Beaches under Future Scenarios”. This paper identifies adaptation tipping points for typical Mediterranean beach types, subject to 50- and 100-year storms, combined with the projected sea-level rise. The research quantifies when conventional measures such as shoreface nourishment, dune reinforcement, or structural protection are bound to lose effectiveness, suggesting the application of adaptation pathways. The results present a time-sequenced approach for proactive decisions, enabling a flexible coastal management and avoiding reactive protection with high impacts and carbon footprint.Based on the improved systemic understanding of beach systems, the second contribution of the thesis is presented in the article “Morphodynamic Predictions Based on Machine Learning: Performance and Limits for Pocket Beaches near the Bilbao Port.” This work analyzes the use of artificial intelligence (machine learning or ML) in coastal morphodynamics. Using high-resolution datasets associated with pocket beaches affected by port interventions, the predictive performance of machine learning models has been compared with conventional methodologies that apply process-based models or traditional statistical techniques. The results show that ML-based models exhibit a high short- and medium-term predictive capacity for capturing complex coastal responses, although their performance is more limited under extrapolated or extreme conditions. This study contributes to integrating coastal engineering with artificial intelligence, providing scalable tools for early warning systems and adaptive coastal monitoring.The cornerstone for sustainable coastal restoration is presented in the third contribution of the Thesis, developed in the article “Coastal Restoration Agreements under Climate Change: Barriers and Enablers.” This work links the analysis of physical processes with governance mechanisms to address the adaptation deficit present in coastal areas. Even when the technical or physical adaptation pathways are known, their implementation faces institutional fragmentation, regulatory rigidity, and social perception. Through an analysis based on application cases, this research identifies the main barriers and enabling factors for coastal restoration under climate uncertainty.
  

DOCTORAL DEGREE IN NATURAL RESOURCES AND THE ENVIRONMENT

• MIRFALLAH LIALESTANI, SEYED POORYA: Geothermal Potential Assessment in Catalonia, Spain: Innovative AI-Powered 3D Mapping using Advanced Hybrid Multitask and Semi-Supervised Learning Frameworks
  
  Author: MIRFALLAH LIALESTANI, SEYED POORYA
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN NATURAL RESOURCES AND THE ENVIRONMENT
  Department: Department of Mining, Industrial and ICT Engineering (EMIT)
  Mode: Normal
  Deposit date: 12/03/2026
  Reading date: 21/05/2026
  Reading time: 11:00
  Reading place: Sala d'actes Escola Politècnica Superior d'Enginyeria de Manresa
  Thesis director: HIMI BENOMAR, MAHJOUB
  Thesis abstract: This dissertation introduces an innovative interdisciplinary framework that merges artificial intelligence with metaheuristic optimization to advance geothermal energy exploration in Catalonia, Spain. By integrating machine learning techniques such as deep learning, neural networks, and decision trees with advanced optimization methods including the Firefly Algorithm and a newly developed Modified Multi-Objective Bat Algorithm (MoBA), the study significantly improves subsurface temperature prediction accuracy, reduces exploration costs, and enhances geothermal resource assessment and management.The research generates high-resolution 3D geothermal maps at depths of 50, 120, 150, and 180 m, across an extensive area of approximately 7,942 km². The MoBA demonstrated exceptional performance in forecasting subsurface temperatures, outperforming conventional methods by adapting to complex and variable geological conditions, minimizing manual intervention, and balancing multiple optimization objectives. This approach enabled the identification of high-potential zones, particularly in Vallès and Penedès, La Selva, Empordà, Fossa d’Olot, Plana de Vic, and the Central Depression.Key findings reveal that geothermal potential increases significantly with depth, with average estimated capacities rising from 15,408.5 MWh/year at 50 m to 19,260.6 MWh/year at 150 m. Approximately 24% of surveyed locations were classified as “very good,” indicating strong suitability for small to medium-scale geothermal systems. The results also confirmed a weak correlation between thermal conductivity and geothermal gradient (r² = 0.02), highlighting the geological heterogeneity of the region and the importance of site-specific evaluations.The AI-driven models aligned closely with prior research (e.g., Colmenar-Santos et al., 2016), validating the reliability of this approach while offering greater spatial detail and resolution. MoBA-based predictions and spatial distribution maps offer actionable insights for policymakers and energy planners, identifying zones suitable for district heating, hybrid systems, and decentralized renewable energy integration.Despite challenges such as data availability and computational demands, the integration of AI with metaheuristic algorithms presents a scalable, cost-effective, and precise method for geothermal exploration. This work not only contributes a powerful tool for sustainable resource exploitation in Catalonia but also provides a transferable model for enhancing renewable energy strategies in other geologically diverse regions.
  

DOCTORAL DEGREE IN NETWORK ENGINEERING

• GUZMÁN ALBIOL, MARC: An Exploration of Constraint Systems in Verifiable Computation
  
  Author: GUZMÁN ALBIOL, MARC
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN NETWORK ENGINEERING
  Department: Department of Network Engineering (ENTEL)
  Mode: Normal
  Deposit date: 23/04/2026
  Reading date: 03/06/2026
  Reading time: 11:00
  Reading place: sala Aula Màster del C3 (Sala C3005
  Thesis director: MUÑOZ TAPIA, JOSE LUIS
  Thesis abstract: The accelerated adoption of digital services has highlighted the need for trust-minimized computation, where parties can verify the correctness of computations without re-executing them or revealing sensitive data. Zero-knowledge proof systems, including SNARKs and STARKs, provide cryptographic guarantees of correctness, privacy, and succinct verifiability, enabling applications in scalable blockchains, privacy-preserving identity systems, and verifiable federated learning.This thesis addresses key inefficiencies in constraint-based zero-knowledge proof systems at the arithmetization layer. The research focuses on two complementary problems: optimizing binary comparisons within Rank-1 Constraint Systems (R1CS), and extending the expressiveness of STARKs through an Extended Algebraic Intermediate Representation (eAIR).The first contribution presents a weighted accumulation method for implementing strict binary comparisons in R1CS. Traditional approaches generate a large number of constraints due to the lack of native comparison and control-flow operations in the R1CS model, forcing costly bit-by-bit decompositions and creating performance bottlenecks. The proposed weighted accumulation method significantly reduces constraint overhead without compromising system security or correctness, achieving substantial efficiency improvements over thelexicographic approach.The second contribution introduces the eSTARK protocol, which extends standard STARKs by enabling the concise handling of complex constraints such as lookups, permutations, and copy constraints. These operations are difficult to encode efficiently in standard AIR. The eSTARK protocol integrates vector commitment arguments and polynomial optimizations, providing a flexible and user-friendly framework for representing a broader class of computations without introducing unnecessary arithmetization overhead.Both contributions address practical limitations of current zero-knowledge proof systems. The first focuses on reducing constraint complexity for common operations, while the second expands the expressiveness of the proof system itself. Together, they demonstrate the importance of arithmetization-level optimizations for improving the efficiency and usability of zero-knowledge proofs.
  

DOCTORAL DEGREE IN PHOTONICS

• MALLA, ADITYA JAGADEESH: COLLOIDAL QUANTUM DOT EMITTERS IN THE SHORTWAVE INFRARED REGION
  
  Author: MALLA, ADITYA JAGADEESH
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN PHOTONICS
  Department: Institute of Photonic Sciences (ICFO)
  Mode: Normal
  Deposit date: 16/04/2026
  Reading date: 29/05/2026
  Reading time: 10:00
  Reading place: ICFO Auditorium
  Thesis director: KONSTANTATOS, GERASIMOS
  Thesis abstract: Shortwave infrared (SWIR) light sources are indispensable for applications including advanced imaging, spectroscopy, and sensing; however, their widespread adoption is hindered by the high cost and limited scalability of epitaxial semiconductor technologies such as InGaAs. Colloidal quantum dots (QDs) offer an attractive alternative owing to their high photoluminescence quantum yield, size-tunable emission, large-area processability, and compatibility with low-cost solution-based fabrication. Among various QD-based emitters employing lead sulphide (PbS), this thesis focuses on two complementary technologies: electrically driven quantum-dot light-emitting diodes (QLEDs) and optically pumped downconverters (DCs).The first part of this thesis addresses performance enhancement in QLEDs (emitting at 1380 nm) through systematic device engineering. Charge imbalance is identified as a key factor limiting QLED efficiency and radiance. By optimising the ZnO electron transport layer via controlled annealing-temperature tuning, electron injection was modulated, leading to a maximum external quantum efficiency (EQE) of 20%. Furthermore, the charge balance within the emissive layer was optimised by controlling its thickness, resulting in an increase in maximum radiance from 5 W.sr-1.m-2 to 17.5 W.sr-1.m-2. Building upon this, a dual electron transport layer architecture was implemented to decouple interfacial quality from bulk electron transport, enabling a further enhancement in maximum radiance to 30 W.sr-1.m-2 while maintaining comparable EQE.Light extraction and Joule heating constitute an additional bottleneck in achieving high-performance QLEDs. To overcome substantial optical losses into substrate modes inherent in conventional bottom-emission devices, top-emission QLED (TQLED) architectures were investigated. These offer improved light extraction and allow for the use of opaque, high-thermal-conductivity silicon substrates to manage Joule heating. A high-performance sputtered indium tin oxide (ITO) electrode was developed, exhibiting optical transmission exceeding 85% at 1400 nm and a low sheet resistance of 33 Ω/□. By utilising optimised architecture with integrated ITO optical spacers and a dielectric/metal/dielectric top electrode, a low-Q microcavity was established. This modified the far-field radiation pattern to a forward-directed profile and narrowed the emission linewidth. The synergy between this resonant optical design and superior thermal dissipation enabled a record radiance exceeding 100 W.sr-1.m-2, and allowed for the first demonstration of active see-through SWIR imaging illuminated solely by QLEDs.The second part of the thesis is focused on lead sulphide QD-based DCs. The QD-DCs suffer from performance degradation under high excitation power densities due to the significant heat generation in the process of light absorption. We have developed high-power, stable, and spectrally tunable narrowband and broadband SWIR DCs (1000 nm - 1600 nm). By mixing two different-sized QDs, we exploit Förster resonance energy transfer and photon reabsorption to realise a binary system with a high photoluminescence quantum yield of 35 %. Embedding the QDs in a poly(methyl methacrylate) host mitigates local thermal stress on the QDs, enabling standalone DCs with a high emission power density (EmPD) of 110 mW.cm-2 at 1380 nm. Further optimisation with a spectrally selective distributed Bragg reflector for enhanced light extraction and a sapphire substrate for efficient heat dissipation, we achieved a record EmPD of 385 mW.cm-2 at 1380 nm with optical power conversion efficiency of 10% and operational stability above 230 hours at an EmPD of 190 mW.cm-2. This demonstrates a scalable route to low-cost SWIR light sources, narrowing the performance gap between solution-processed DCs and conventional epitaxial semiconductors.
  
• POPOV, PAVEL PEYCHEV: Quantum simulation of lattice gauge theories with qudit systems
  
  Author: POPOV, PAVEL PEYCHEV
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN PHOTONICS
  Department: Institute of Photonic Sciences (ICFO)
  Mode: Normal
  Deposit date: 29/04/2026
  Reading date: 29/05/2026
  Reading time: 10:30
  Reading place: Elements Room i https://teams.microsoft.com/meet/339611996180202?p=2I9fTirIejfsIaqwoW
  Thesis director: LEWENSTEIN, MACIEJ | KASPER, VALENTIN
  Thesis abstract: The spectacular progress in controlling quantum matter has opened new avenues for studying fundamental physics. Various experimental platforms now host hundreds of quantum units, capable of quantum state engineering, Hamiltonian simulation and universal computation, already surpassing what is classically tractable. Remarkably, the versatility of such quantum simulators allows for investigating the physics from very high to very low energy scales. While the long-term goal is to be able to perform fault-tolerant quantum computation, noisy intermediate scale quantum (NISQ) devices are prone to errors and quantum algorithms need to be tailored to the underlying physical platform by exploiting its advantages. In that regard, qudits offer enhanced Hilbert space dimension per information carrier with respect to qubits, allowing for significant reduction of costly entanglement operations. Moreover, the higher-dimensional Hilbert space of qudits natively accommodates complex many-body models, thereby minimizing algorithmic overhead.In this thesis, we investigate the opportunities that qudit devices offer for the quantum simulation of lattice gauge theories. Being extremely successful nonperturbative framework for studying three of the four fundamental interactions--electrodynamics, the weak and the strong force-- lattice gauge theories can be formulated as many-body systems amenable to quantum simulation. This approach overcomes the intrinsic bottlenecks of classical methods, unlocking the ability to explore out-of-equilibrium phenomena and finite-density equilibrium states.The first part of this thesis is dedicated to the development of encoding procedures for lattice gauge theories with Abelian and non-Abelian symmetry on qudit quantum hardware. Building upon advances in the understanding of the structure of the gauge-invariant Hilbert space for specific symmetry groups, we propose scalable qudit implementation of gauge theory models in arbitrary spatial dimensions and devise variational protocols for their equilibrium and out-of-equilibrium simulation. Crucially, our methods apply to gauge theories with dynamical fermionic matter, without the need for nonlocal encodings for the fermions, as they are unitarily removed in the encoding process. In the second part of this thesis, we use quantum-inspired numerical techniques to reveal some of the plethora of physical phenomena simple many-body models with local symmetry host. Using the multi-flavour Schwinger model (quantum electrodynamics in one spatial dimension) as an example, we show how to identify signatures of fractons — gauge field configurations with fractional topological charge. Furthermore, by examining pure gauge theories with non-Abelian dihedral symmetry, we identify the importance of the central subgroup for the spectrum and the dynamics of the many-body model, relating nontrivial fusion rules to lack of confinement and presence of exotic particle excitations. Most importantly, the lattice gauge models for both examples above, due to their simplicity, are amenable to near-term implementation on qudit quantum hardware.Ultimately, this work takes a significant step toward harnessing qudit quantum devices for the simulation of high-energy and condensed-matter systems. By detailing resource-efficient hardware implementations and outlining near-term applications, our findings provide compelling motivation for the continued symbiosis of theoretical design and experimental realization.
  

DOCTORAL DEGREE IN SIGNAL THEORY AND COMMUNICATIONS

• AREIAS FANZERES, LEONARDO: Sound-to-Image Translation Through Direct Cross-Modal Learning: An Exploratory and Architectural Study
  
  Author: AREIAS FANZERES, LEONARDO
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN SIGNAL THEORY AND COMMUNICATIONS
  Department: Department of Signal Theory and Communications (TSC)
  Mode: Normal
  Deposit date: 16/04/2026
  Reading date: 18/06/2026
  Reading time: 11:00
  Reading place: Aula de Teleensenyament, edifici B3, Campus Nord, Barcelona
  Thesis director:
  Thesis abstract: Environmental sound conveys rich semantic and contextual information about events, objects, and spatial dynamics. However, prevailing computational approaches to environmental audio analysis, such as Acoustic Event Detection (AED), typically reduce this complexity to discrete textual labels. While effective for automated monitoring tasks, such representations oversimplify acoustic scenes and become inadequate when auditory information must be communicated across modalities. Sound-to-image (S2I) translation offers an alternative approach in which a model synthesizes images that visually depict sound-emitting sources and their surrounding environments.This thesis introduces and advances direct sound-to-image translation, a paradigm that establishes a connection between audio and visual modalities without relying on textual mediation, class supervision, or cluster-based alignment during training. The central hypothesis is that higher-level abstractions learned by deep neural networks provide a shared semantic space in which heterogeneous modalities can connect directly, enabling the generation of images that are interpretable and semantically coherent with the source sound. Such outputs are referred to as informative, meaning that they visually communicate meaningful aspects of the acoustic event.The first part of the thesis presents, to the best of our knowledge, the first study dedicated to direct S2I translation. A densely connected generative adversarial network (GAN), conditioned on audio embeddings, is developed to synthesize images directly from sound. Because multiple plausible images may correspond to a single acoustic event, translation quality cannot be evaluated through pixel-level reconstruction. To address this challenge, an informativity-based evaluation framework is proposed, employing classifiers to determine whether generated images are interpretable and semantically coherent with the source audio. Experiments reveal that, despite the inherent difficulty of the task, the model generalizes to unseen sounds and produces informative outputs for a meaningful portion of translations. Analysis further reveals that latent bottleneck dimensionality influences translation behavior, exposing a trade-off between pixel-space convergence and informativity.Building on this foundation, the second part investigates whether attention mechanisms can strengthen cross-modal alignment. Self-attention and cross-attention modules are integrated into the generator and evaluated across multiple configurations. Results show that attention improves translation performance when applied at early stages of the network, increasing informativity relative to the purely convolutional baseline while preserving the fully direct audio–visual connection.Together, these contributions formally define, validate, and extend direct S2I translation as a distinct research paradigm. Beyond architectural design and training strategies, this thesis advances methodological principles for quantitative evaluation of cross-modal translation in the absence of deterministic visual ground truth. The findings contribute to a broader understanding of multimodal representation learning and highlight the potential of direct S2I translation for applications in multimodal interaction and accessibility-oriented technologies, particularly for enhancing situational awareness in deaf and hard-of-hearing individuals.
  
• LIU, JINGLONG: Estimation of Crop Above Ground Biomass Density (AGBD) based on the Integration of Remote Sensing Data and Crop Growth Models
  
  Author: LIU, JINGLONG
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN SIGNAL THEORY AND COMMUNICATIONS
  Department: Department of Signal Theory and Communications (TSC)
  Mode: Normal
  Deposit date: 15/04/2026
  Reading date: 21/05/2026
  Reading time: 12:00
  Reading place: TSC Aula MERIT D5010, Campus Nord, Barcelona
  Thesis director: MALLORQUI FRANQUET, JORDI JOAN
  Thesis abstract: This thesis addresses the limitations of traditional remote sensing (RS) and crop growth models (CGMs) in estimating crop Above-Ground Biomass Density (AGBD). While optical RS is constrained by cloud cover and signal saturation, and CGMs often require complex calibration and struggle to capture spatial heterogeneity, their integration offers a promising strategy for continuous monitoring.The research enhances AGBD estimation through three main contributions: (1) The development of an Improved Parametric Model (IPAM) incorporating Fibonacci-based corrections to improve accuracy and reduce overestimation; (2) A novel algorithm for retrieving crop height and AGBD relying solely on SAR dual-polarization backscattering; and (3) A Data Assimilation system integrating RS observations into a modified WOFOST model, validated using multi-year datasets from two farms in the USA. The results demonstrate that integrating RS with CGMs significantly improves the interpretability, transferability, and operational reliability of large-scale crop biomass estimation.
  
• MOSAHEBFARD, MOHAMMADREZA: Resource Management in Sliced Converged Optical-Wireless 6G Networks: From Strategic Dimensioning to Dynamic Service Provisioning
  
  Author: MOSAHEBFARD, MOHAMMADREZA
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN SIGNAL THEORY AND COMMUNICATIONS
  Department: Department of Signal Theory and Communications (TSC)
  Mode: Normal
  Deposit date: 10/04/2026
  Reading date: 29/05/2026
  Reading time: 11:00
  Reading place: Room C4-021B at the Castelldefels Campus
  Thesis director: VERIKOUKIS, CHRISTOS | VARDAKAS, JOHN
  Thesis abstract: The evolution toward the 6th Generation (6G) networks relies on converged optical-wireless infrastructures and virtualization technologies like Network Functions Virtualization (NFV) to support diverse services through Network Slicing. While enabling flexibility, these paradigms introduce profound complexity in managing shared computational and com-municational resources. This thesis addresses this challenge by identifying a fundamental temporal duality in resource management, spanning the strategic need for agile capacity planning to the operational necessity of real-time service provisioning. This complexity is further aggravated by the presence of various network slices, each with distinct Quality of Service (QoS) requirements. To resolve this dichotomy, distinct methodologies are developed within this PhD thesis.To address the strategic domain, where Mobile Virtual Network Operators (MVNOs) require rapid dimensioning tools for flexible resource leasing, this PhD thesis develops a computationally efficient analytical framework based on one-dimensional Markov chains. Uniquely, this model captures resource occupancy at two levels: physical resources governing Service Function Chain (SFC) instantiation, and virtual resources governing user admission. This stratified modeling avoids state-space explosion while accurately calculating admission ratios, achieving relative errors typically below 2% compared to simulations. The framework is applied to determine the minimum resources required to achieve target admission ratios under varying arrival rates, as well as to identify the necessary capacity to meet different target admission rates under constant traffic loads. This capability enables rapid offline dimensioning to guarantee Quality of Service (QoS) thresholds, offering a scalable alternative to time-consuming simulations and resource-exhaustive optimization approaches.Complementing this, the operational challenge of online SFC Embedding (SFCE) is addressed. Recognizing the NP-hardness of the embedding problem with the objective of jointly minimizing holistic power consumption and blocking probability, HORIZON, a novel holistic heuristic designed to optimize Mobile Virtual Network Operator (MVNO) operational efficiency, is developed. It employs a proactive backward placement strategy coupled with power and latency-aware segmental routing, and integrates comprehensive power models for both servers and Reconfigurable Optical Add-Drop Multiplexers (ROADMs). By enforcing strict inter-slice isolation while maximizing intra-slice efficiency, HORIZON achieves power efficiency within 15% of optimal Integer Linear Programming (ILP) bounds under resource-constrained scenarios. Evaluations across four realistic network topologies demonstrate execution times of 10–18 ms per service request (38–67 times faster than optimal solvers), power savings up to 23.6% compared to state-of-the-art heuristics, and negligible blocking rates, enabling MVNOs to minimize OPerational EXpenditure (OPEX) while strictly adhering to Service Level Agreements (SLAs).
  
• RUIZ CARREGAL, GERARD: High-Resolution Drone-Based Repeat-pass SAR Interferometry for 3D displacement estimation
  
  Author: RUIZ CARREGAL, GERARD
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN SIGNAL THEORY AND COMMUNICATIONS
  Department: Department of Signal Theory and Communications (TSC)
  Mode: Article-based thesis
  Deposit date: 15/04/2026
  Reading date: 27/05/2026
  Reading time: 11:00
  Reading place: Sala D4 012, UPC Campus Nord.
  Thesis director: LOPEZ MARTINEZ, CARLOS | IGLESIAS GONZÁLEZ, RUBÉN | LORT CUENCA, MARC
  Thesis abstract: The precise characterization of ground deformation processes is essential for risk assessment and early-warning applications, since displacement is often a precursor to catastrophic failures. Over the past two decades, Synthetic Aperture Radar (SAR) and Multi-Temporal Differential SAR Interferometry (MT-DInSAR) have enabled millimetric displacement estimation over wide areas with dense spatial sampling. Within this context, satellite-based SAR offers regular, long-term global coverage, while Ground-Based SAR (GBSAR) provides near real-time monitoring, enabling the observation of localized rapid deformation processes. Despite these strengths, both systems present inherent limitations. Satellite SAR cannot capture rapid displacements due to multi-day revisit intervals and provides no sensitivity to displacement along the North-South (NS) direction due to its near-polar orbits. GBSAR provides excellent temporal resolution, but its deployment is limited in inaccessible areas, and its fixed geometry often leads to displacement underestimation.Airborne SAR systems mitigate several of these limitations by offering controlled revisit times and multi-view imaging, allowing the retrieval of the three-dimensional (3D) displacement vector. In this context, multirotor drones have emerged as a cost-effective and adaptable platform for airborne SAR. Drone-borne SAR inherits airborne challenges related to platform motion and navigation uncertainties, together with additional constraints such as payload limitations, and requires the adaptation of MT-DInSAR algorithms to low-altitude platforms and non-regular revisit times.The objective of this dissertation is to demonstrate the capability of drone-based SAR systems to estimate ground displacement time-series using multi-temporal interferometric stacks in real operational scenarios. To this end, the thesis develops an end-to-end framework encompassing sensor development, interferometric processing, and MT-DInSAR methodologies. A Ku-band dual-channel Frequency-Modulated Continuous-Wave (FMCW) radar is developed and integrated into a multirotor platform. An interferometric processing chain is further proposed to generate phase-calibrated repeat-pass interferograms, combining Digital Elevation Model (DEM) refinement from single-pass interferometry with a dedicated coregistration strategy, where the MTCD-MSQ algorithm is introduced as a new coregistration approach designed for high-frequency airborne acquisitions. Furthermore, two complementary MT-DInSAR workflows are proposed to address distinct deformation regimes. SD-MT-DInSAR retrieves cumulative displacement time-series when interferometric coherence is preserved between consecutive acquisitions, while SDVEL-MT-DInSAR exploits the flexibility of drones to perform repeated intra-day flights to estimate displacement velocity time-series in rapidly deforming areas, where decorrelation occurs in a few hours. Finally, the thesis employs multi-geometry acquisitions to retrieve the full 3D displacement vector.The framework is validated in controlled experiments with Corner Reflectors (CR) and in real operational conditions over an active open-pit mine, demonstrating submillimetric sensitivity, meter-scale displacement monitoring over several days, and 3D displacement retrieval in complex scenarios.The dissertation confirms that drone-based SAR is a reliable deformation monitoring tool that complements satellite and GBSAR systems, opening new opportunities in geotechnics, mining, and natural hazard assessment.
  

DOCTORAL DEGREE IN STRUCTURAL ANALYSIS

• ANTONELLI, NICOLÒ: CFD techniques for IBRA-type discretizations
  
  Author: ANTONELLI, NICOLÒ
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN STRUCTURAL ANALYSIS
  Department: Department of Civil and Environmental Engineering (DECA)
  Mode: Article-based thesis
  Deposit date: 21/04/2026
  Reading date: 18/05/2026
  Reading time: 12:00
  Reading place: Sala Zienkiewich (CIMNE) Building C1, UPC - Campus North Gran Capitan S/N 08034 Barcelona
  Thesis director: ROSSI BERNECOLI, RICCARDO | ZORRILLA MARTÍNEZ, RUBÉN
  Thesis abstract: Isogeometric Analysis (IGA) enables exact geometry representation from CAD and provides high-order, highly continuous approximation spaces. Despite these advantages, its industrial adoption remains limited due to the multipatch structure of CAD models and the difficulty of constructing analysis-suitable volumetric parameterizations. Embedded and trimming-based IGA techniques alleviate meshing constraints but inherit the small cut-cell problem, often leading to severe ill-conditioning and degraded solver performance.This thesis investigates whether the cut-cell-free philosophy of the Shifted Boundary Method (SBM) can be transferred to the isogeometric setting to combine geometric fidelity, high-order accuracy, and robust conditioning. A penalty-free SBM--IGA formulation for elliptic problems is first developed. Optimal convergence rates are obtained for Dirichlet boundary conditions, and the resulting systems exhibit the expected condition number scaling, avoiding the deterioration typical of trimming-based approaches.The framework is then extended to mixed formulations and incompressible Stokes flows, including non-Newtonian viscoplastic models. Finally, the Gap--Shifted Boundary Method is generalized to nonconforming multipatch coupling. Arbitrary mesh mismatch, polynomial degree heterogeneity, and local refinement are supported without additional degrees of freedom, while maintaining optimal convergence and favorable conditioning.Overall, the thesis establishes a geometrically flexible and inherently well-conditioned embedded isogeometric framework suitable for CAD-integrated simulation.
  

DOCTORAL DEGREE IN SUSTAINABILITY

• LARVOE, NOAH: FROM FARM TO FORK: THE ROLE OF FARMERS AND CONSUMERS IN PESTICIDE REDUCTION IN EUROPEAN VINEYARDS AND OLIVE GROVES
  
  Author: LARVOE, NOAH
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN SUSTAINABILITY
  Department: University Research Institute for Sustainability Science and Technology (IS.UPC)
  Mode: Article-based thesis
  Deposit date: 10/04/2026
  Reading date: 18/05/2026
  Reading time: 13:00
  Reading place: Sala de Grau, Escola d'Enginyeria Agroalimentària i de Biosistemes de Barcelona-EEABB
  Thesis director: KALLAS CALOT, ZEIN
  Thesis abstract: Sustainability transitions in agri-food systems require coordinated adjustments on both the supply and demand sides of food value chains. In the Mediterranean wine and olive sectors, in which chemical pesticides remain central to pest and disease control, reducing pesticide use in line with the European Union objective of halving synthetic pesticide use by 2030 depends on both consumer support for reduced pesticide products and farmers’ willingness to adopt innovations that make such reductions feasible. This thesis examines whether and under what conditions market demand and farmer behaviour can support pesticide reduction in Mediterranean orchard systems through four empirical studies.The first study analyses consumer preferences for extra virgin olive oil with reduced pesticide use using a reference-dependent contingent valuation survey of 5,091 consumers in France, Greece, Italy, Portugal, and Spain. Approximately 73% of respondents are willing to pay a premium for olive oil produced with a 40% reduction in pesticide use. The estimated mean premiums range from 22% in Italy to 26% in France relative to products produced under standard practices. The second study examines organic wine consumers to estimate the proportion of the organic price premium attributable to the non-use of synthetic pesticides. It employs a reference price-dependent discrete choice experiment (DCE) with 1,070 consumers. The results show that the non-use of synthetic pesticides accounts for 74% of the average reference organic price reported in the study, exceeding the contribution of other organic attributes, while the EU-approved third-party certification generates the highest positive effect. Latent class analysis identifies three consumer segments, namely, awareness-driven, certification-driven, and price-sensitive consumers, which differ systematically in attitudes, knowledge, and purchasing experience. The third study extends the analysis to the conventional wine market using a dynamic reference-dependent DCE with 4,000 consumers in the same countries. The results show that wines produced with a 40% reduction in pesticide use command significant price premiums, ranging from approximately 26% in France to 30% in Italy and Portugal. Consumers’ environmental attitudes, past purchasing prices, and subjective knowledge significantly influence their preferences, although the strength of these effects varies across countries. The fourth study examines farmers’ intentions to adopt pesticide-reducing innovations using data from 16 expert interviews and a structured survey of 354 orchard farmers within an extended theory of planned behaviour framework. Adoption intentions are driven by positive attitudes toward pesticide reduction and favourable perceptions of ease of use, while perceived costs and beliefs in the superior performance of existing practices constitute significant barriers. Subjective norms and perceived behavioural control do not predict adoption intentions. These findings are used to develop a predictive tool that determines the likelihood of a farmer adopting pesticide-reducing innovations. Overall, the findings suggest that consumers can provide economically meaningful incentives for pesticide reduction in viticulture and olive groves, while farmers’ adoption decisions are shaped mainly by attitudes, perceived complexity, and economic considerations. Policy and stakeholder strategies that combine credible certification, targeted information, and support for innovation costs are likely to strengthen the alignment between market demand and farm-level decision-making. This thesis provides an economic and behavioural assessment of how consumers and farmers can contribute to the EU’s Green Deal objective of reducing pesticide use. Further, it outlines priorities for future research, notably market trials, longitudinal studies of farmer adoption behaviour, and integrated assessments of economic and environmental impacts of pesticide reduction
  
• RHOUMA, ALI: Operationalizing the Water–Energy–Food–Ecosystems Nexus for the Sustainability Assessment of Mediterranean Farming Systems
  
  Author: RHOUMA, ALI
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN SUSTAINABILITY
  Department: University Research Institute for Sustainability Science and Technology (IS.UPC)
  Mode: Normal
  Deposit date: 24/04/2026
  Reading date: pending
  Reading time: pending
  Reading place: pending
  Thesis director: GIL ROIG, JOSE MARIA | BROUWER, FLOOR
  Thesis abstract: Agricultural systems face increasing pressures from water scarcity, climate change, and environmental degradation, particularly in the Mediterranean region where resource constraints are intensifying. Addressing these interconnected challenges requires integrated analytical approaches capable of capturing the complex interactions between water, energy, food production, and ecosystems. The Water–Energy–Food–Ecosystems Nexus has emerged as a promising framework for supporting sustainable resource management; however, its operationalization at the farm level remains limited. This thesis aims to advance the application of the WEFE Nexus for the sustainability assessment of farming systems by developing an integrated analytical framework and practical evaluation tools.The research adopts a progressive methodological approach. First, water-related sustainability indicators specifically the water footprint and water scarcity footprint are applied to assess the pressures of agricultural production on water resources. These indicators provide an initial understanding of resource use efficiency and highlight critical water-related challenges in farming systems. Building on this foundation, the thesis develops a WEFE Nexus assessment framework based on system dynamics modelling to capture interactions between water use, energy consumption, food production, and ecosystem impacts. The framework is implemented through a user-friendly decision-support tool designed to support sustainability assessments at the farm level.The developed framework is subsequently applied to evaluate WEFE Nexus solutions in agricultural systems, with a particular focus on agroecological practices. By integrating multiple indicators related to resource efficiency, environmental performance, and agricultural productivity, the analysis explores how agroecological approaches influence the performance of farming systems within the WEFE Nexus. The results demonstrate that agroecological practices can improve resource use efficiency, reduce environmental pressures, and enhance the resilience of farming systems.Finally, the outcomes of the WEFE Nexus assessment are translated into key performance indicators linked to the Sustainable Development Goals (SDGs). This step enables the quantification of how WEFE-based agricultural practices contribute to broader sustainability objectives and global development targets. By linking farm-level sustainability assessment with the SDG framework, this research provides a novel methodological contribution for evaluating the sustainability impacts of agricultural practices.This thesis contributes to bridging the gap between WEFE Nexus theory and practical agricultural sustainability assessment. The proposed framework offers a robust approach for evaluating sustainable farming systems and provides valuable insights for policymakers, researchers, and stakeholders seeking to promote resilient and resource-efficient agriculture in the Mediterranean region and beyond.
  
• VILLANUEVA ESCOBEDO, BRENT: Design and Application of an AI-Enabled Adaptive Framework for Assessing Circularity in Socio-Ecological and Technical Systems (SETs) within the WEFE Nexus
  
  Author: VILLANUEVA ESCOBEDO, BRENT
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN SUSTAINABILITY
  Department: University Research Institute for Sustainability Science and Technology (IS.UPC)
  Mode: Normal
  Deposit date: 14/04/2026
  Reading date: 17/07/2026
  Reading time: 10:30
  Reading place: UPC ESEIAAT edifici TR1Planta 0 Porta 085
  Thesis director: MORATO FARRERAS, JORDI
  Thesis abstract: Sustainability and circular economy transitions in contemporary socio-environmental systems are increasingly challenged by complexity, uncertainty, and strong interdependencies between natural resources, infrastructures, and governance structures. Traditional sectoral and indicator-based assessment approaches have proven insufficient to capture these interactions, often overlooking systemic trade-offs, emergent behaviors, and context-specific constraints. In response to these limitations, this doctoral thesis develops, applies, and validates integrated, participatory, and adaptive assessment frameworks for sustainability and circularity, grounded in systems thinking and the Water–Energy–Food–Ecosystems (WEFE) Nexus.The thesis is structured as a compendium as follows. The first chapter is the introduction, including the research context of the main concepts, the background and rationale, and the design, starting with the research questions, hypothesis, identification of the research gap, the research lines and objectives. The second chapter, dives into the state of the art of the main concepts, presenting the bibliometric analysis as well as the institutional context and the projects that provided the scene for the applied research. The third chapter is dedicated to the methodology, starting with the global methodology framework and then focusing on the WEFE Nexus and integrated assessment. The fourth chapter, proposes an integrated multi-criteria framework for assessing sustainability and circular economy performance in public water utilities operating in Mediterranean contexts, combining environmental footprints, ecosystem services, governance, and operational performance within a decision-oriented analytical structure. The fifth chapter applies an artificial intelligence (AI)–enabled WEFE Nexus assessment tool to evaluate circular bioeconomy (specifically biochar and agroforestry) highlighting trade-offs, synergies, and context-dependent outcomes under future scenarios in a dynamic way. The sixth chapter consolidates these insights into a transferable methodological framework for participatory, AI-enabled circularity assessment across heterogeneous socio-environmental systems. The seventh chapter is focused on the discussion of the theoretical and the applied research, specifically to proof whether circularity was implemented as a static set of material loops, or if enough evidence is found that circularity was implemented as an emergent system property, resulting from interactions between resource systems, ecosystems, institutions, and stakeholder values in the frame of this thesis. The eighth and final chapter gathers conclusions to measure the degree in which the methodology and its derived applications help integrate multi-criteria decision analysis, participatory processes, artificial intelligence, and digital twin concepts to support transparent, context appropriate and adaptive sustainability assessment. Finally, limitations and future research lines are drawn to set some basic principles for replication and adaptation of the analysis tool in different contexts. Overall, the thesis aims to contribute in advancing the state of the art in sustainability science by operationalizing the WEFE Nexus through AI-enabled and participatory assessment frameworks, bridging the gap between conceptual integration and practical support for decision-making. The obtained results, demonstrate that systemic, adaptive, and governance-aware approaches are essential for informing transitions towards sustainability and circular economy in complex socio-environmental systems with resource constraints
  

DOCTORAL DEGREE IN TEXTILE AND PAPER ENGINEERING

• LEZECK, HENDRICK: Application of essential oils microcapsules on the fabric surface to get antibacterial properties.
  
  Author: LEZECK, HENDRICK
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN TEXTILE AND PAPER ENGINEERING
  Department: Department of Engineering Graphics and Design (DEGD)
  Mode: Normal
  Deposit date: 28/04/2026
  Reading date: pending
  Reading time: pending
  Reading place: pending
  Thesis director: LIS ARIAS, MANUEL JOSÉ
  Thesis abstract: Essential oils (E.O.) are widely used in traditional medicine, pharmacy, food, and cosmetic applications due to their natural origin, biodegradability, and broad antimicrobial activity. Despite these advantages, their high volatility and hydrophobic nature significantly limit their direct application in textile substrates. In recent years, the increasing demand for sustainable, functional textiles has driven research to integrate bioactive compounds into fabrics while preserving their efficacy and durability.This doctoral thesis investigates the application of essential oil microcapsules onto textile substrates as a strategy to overcome the intrinsic limitations of E.O. and to impart bio-functional properties to fabrics. By employing microencapsulation techniques, this work aims to enhance the stability, retention, and controlled release of essential oils on textiles, enabling the development of sustainable materials with long-lasting bioactive performance.
  

DOCTORAL DEGREE IN URBAN AND ARCHITECTURAL MANAGEMENT AND VALUATION

• ESPINOZA ZAMBRANO, PAÚL ANDRÉS: Libro del Edificio Electrónico (LdE-e). Una herramienta para impulsar la rehabilitación de edificios residenciales en España
  
  Author: ESPINOZA ZAMBRANO, PAÚL ANDRÉS
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN URBAN AND ARCHITECTURAL MANAGEMENT AND VALUATION
  Department: Department of Architectural Technology (TA)
  Mode: Article-based thesis
  Deposit date: 29/04/2026
  Reading date: 01/06/2026
  Reading time: 16:30
  Reading place: ETSAB (Escuela Técnica Superior de Arquitectura de Barcelona) - Planta Baja - Sala de GradosAv. Diagonal, 649-651 - 08028 - Barcelona
  Thesis director: MARMOLEJO DUARTE, CARLOS RAMIRO
  Thesis abstract: The climate emergency and the European Green Deal call for transforming the building sector, a key area in energy consumption and emissions. In Spain, the challenge is greater due to an aging and inefficient residential stock, compounded by the complex management of horizontal property regimes. Despite regulatory progress, energy rehabilitation remains stagnant. This thesis argues that the problem is not solely technical or economic, but a market failure caused by information asymmetry and documentary fragmentation. "Information vortices" prevent the proper valuation of energy efficiency, perpetuating inaction.To address this, the e-Building Logbook (LdE-e) is proposed as an integrated information management model. The model combines two tools from Directive (EU) 2024/1275: the Digital Building Logbook (DBL), as a static technical archive, and the Building Renovation Passport (BRP), as a dynamic roadmap for staged renovations. Its key innovation lies in its technological development using BIM and Blockchain. An architecture is designed to extract data from IFC files to avoid redundancies, alongside a tokenization gateway ensuring data immutability, traceability, and legal validity.The methodology combines qualitative and quantitative phases. First, the model was validated with 21 experts from the sustainable real estate sector, refining its structure, management, and governance —including the proposal of an LdE-e Consortium— and adapting the technology for professional usability.In the quantitative phase, a social perception study was conducted through a survey of 4,041 households across 14 metropolitan areas. Advanced techniques were applied, including Latent Class Analysis (LCA) to profile adoption patterns, and Bayesian-optimised Dense Neural Networks (DNN) to model the complexity of household decision-making.The results reveal two key findings. First, technology is necessary but insufficient: the decisive factor for LdE-e adoption is support through One-Stop Shops (OSS), which transform hesitant users into active adopters. Second, financial stress shapes motivations: households with low energy costs prioritize environmental or lifestyle values, while vulnerable ones act out of economic pragmatism, seeking subsidies and property aesthetic improvements.In conclusion, this thesis provides a robust LdE-e model aligned with European regulations, resolving the information fragmentation in the Spanish market. Its operational viability depends on integration into a socio-technical ecosystem where BIM and Blockchain provide technical security, and One-Stop Shops (OSS) offer the trust and human support needed to mobilize citizens toward decarbonization. The combination of advanced technology and personalized support emerges as the essential formula to overcome current barriers and activate the virtuous circle of energy rehabilitation in Spain's residential stock.
  

NAUTICAL ENGINEERING, MARINE AND NAVAL RADIOELECTRONICS

• ORTEGA OLIVA, NARCISO: FRONTEX Y LA FRONTERA MEDITERRÁNEA: UN NUEVO MODELO PARA RECUPERAR LA HUMANIDAD EN EL MAR
  
  Author: ORTEGA OLIVA, NARCISO
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: NAUTICAL ENGINEERING, MARINE AND NAVAL RADIOELECTRONICS
  Department: Department of Nautical Sciences and Engineering (CEN)
  Mode: Normal
  Deposit date: 13/03/2026
  Reading date: 21/05/2026
  Reading time: 12:00
  Reading place: Sala de Juntes de la FNB
  Thesis director: RODRIGO DE LARRUCEA, JAIME
  Thesis abstract: The report provides a critical and reasoned analysis of the evolution of FRONTEX and European border management, combining legal, political, sociological and operational aspects. It uses a robust methodology with empirical data, fieldwork, international comparative analyses, advanced statistical tools such as multiple correspondence analysis and Chi-square tests, as well as graphs and tables that facilitate understanding of the results. It identifies structural problems such as covert militarisation, the externalisation of responsibilities and the crisis of legitimacy of FRONTEX, and proposes concrete and viable solutions for structural reform towards a more ethical, transparent and human rights-friendly model. It aims to contribute to the academic and legal debate on European border management with scientific rigour.
  

DOCTORAL DEGREE IN APPLIED MATHEMATICS

• GIL RAMS, DÍDAC: Splitting of separatrices in generalized standard maps
  
  Author: GIL RAMS, DÍDAC
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN APPLIED MATHEMATICS
  Department: School of Mathematics and Statistics (FME)
  Mode: Normal
  Deposit date: 15/05/2026
  Deposit END date: 29/05/2026
  Thesis director: MARTIN DE LA TORRE, PAU | BALDOMA BARRACA, INMACULADA CONCEPCION
  Thesis abstract: We consider generalized standard maps, that is, families of area-preserving maps on the plane F:(x,y) = (x+y+f(x;h),y+f(x;h)) with h the perturbative parameter and f fulfilling quite general assumptions. This generalization includes the Chirikov standard map, the Hénon map or the perturbed McMillan map, among others.We study transverse intersections between the invariant manifolds (stable and unstable) associated to a hyperbolic fixed point of F. These intersections give rise to homoclinic orbits related to the hyperbolic fixed point. The existence of these kind of orbits is one of the most celebrated methods to prove the existence of chaotic dynamics in a system. Indeed, the Birkhoff-Smale Homoclinic Theorem ensures that, if there exist transverse intersections between the invariant manifolds of the same invariant object, the system is locally conjugate to a Smale horseshoe with infinite symbols.The classical Melnikov theory is a first order perturbative theory that is often used to measure the intersection angle between the invariant manifolds. However, when the Melnikov function is exponentially small in the perturbative parameter, the first order analysis fails. Despite its exponentially small character, the Melnikov function still provides the correct size of the splitting in some systems. This is not our case, which is singular. To deal with it, we use a complex time matching technic involving the inner equation related to F.In the first part of our work, by considering an small enough perturbative parameter h, we obtain an asymptotic formula for the Lazutkin's invariant, a quantity analogous to the intersection angle between the invariant manifolds, related to the primary homoclinic points of a wide class of maps F. An exponentially small upper bound was obtained by Fontich. Later, we provide an asymptotic formula for a controversial example exponentially bigger than the naïve guess.The leading term of the obtained asymptotic formulas depends on a constant, often called Stokes constant, that comes from the study of the inner equation. The second part of our work contains a general algorithm, based on the study of the inner equation related to F, to compute an interval containing such constants by means of a computer assisted proof in CAPD. Finally, we apply this algorithm to prove that the Stokes constants related to maps F of the form f(x;h)=e f_0(x), with e = 4 sinh^2(h/2) and f_0 a polynomial of degree 2<=d_0<= 970 or trigonometric polynomial of any degree, are different from zero (including the Standard and Hénon maps). We also provide an example of a generalized standard map such that its Stokes constant is zero.
  
• KHAN, SHERAZ AHMED: A posteriori error estimates for the finite element approximation of flow problems based on the variational multiscale concept
  
  Author: KHAN, SHERAZ AHMED
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN APPLIED MATHEMATICS
  Department: School of Mathematics and Statistics (FME)
  Mode: Normal
  Deposit date: 15/05/2026
  Deposit END date: 29/05/2026
  Thesis director: CODINA ROVIRA, RAMON
  Thesis abstract: Numerical methods have become popular for solving complex flow problems in recent decades. Finite element methods have attracted the attention of scientists and engineers by offering computational solutions to partial differential equations, widely used to address engineering problems. In fluid mechanics, the standard Galerkin finite element method exhibits unstable and oscillatory solutions when dealing with convection-dominated problems. To overcome these limitations, stabilized finite element methods have emerged that provide stable solutions by incorporating additional stabilization terms into the standard Galerkin formulation. Predicting and controlling the error of numerical approximations remains a computational challenge. A posteriori error estimation has made significant progress and has become an essential tool for finite element practitioners, assessing and controlling the accuracy of numerical solutions and guiding adaptive refinement strategies.The initial part of this study develops a posteriori error estimates for the convection-diffusion-reaction equation using the variational multiscale framework. We present the results of the a priori analysis and two strategies of the a posteriori error analysis for the orthogonal sub-grid scale method. Our proposal is to use a scaled norm of the sub-grid scales as an a posteriori error estimate in the stabilized norm of the problem, which provides control over the convective term. The error convergence analysis is conducted using the L2 norm and the stabilized norm. Numerical examples demonstrate the reliable performance of the proposed error estimator compared to other estimators belonging to the variational multiscale family.The second part develops a goal-oriented a posteriori error estimation framework for linear functionals in the stabilized finite element discretization of the stationary convection-diffusion-reaction equation. We propose an explicit approach using the orthogonal sub-grid scale method, based on evaluating the sub-grid scale in the quantity of interest functionals, and compare it with a duality-based method requiring the solution of an additional adjoint problem. The results indicate that both methods yield similar error estimates, whereas the VMS-based explicit approach is computationally less expensive. Numerical tests demonstrate the effectiveness of the proposed techniques in terms of the quantity of interest functionals.The third part is dedicated to developing an adaptive mesh refinement strategy guided by a posteriori error estimators for the transient Navier-Stokes equations of incompressible flows. A VMS-based a posteriori space error estimator is derived using the orthogonal sub-grid scale method, and an h-refinement strategy driven by the local error indicator is proposed and validated on benchmark flow problems.Overall, this thesis develops a posteriori error estimates for orthogonal sub-grid scale discretizations of flow problems within the variational multiscale framework. The proposed error estimation strategies are shown to be robust for applications in fluid mechanics.
  

DOCTORAL DEGREE IN ARCHITECTURAL, BUILDING CONSTRUCTION AND URBANISM TECHNOLOGY

• MORROS CARDONA, JORDI: Segons ús i costum de bon mestre d’obres. Estudi de les tècniques constructives documentades en les esglésies catalanes durant els segles XVII i XVIII.
  
  Author: MORROS CARDONA, JORDI
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN ARCHITECTURAL, BUILDING CONSTRUCTION AND URBANISM TECHNOLOGY
  Department: Department of Architectural Technology (TA)
  Mode: Normal
  Deposit date: 14/05/2026
  Deposit END date: 28/05/2026
  Thesis director: ONECHA PEREZ, ANA BELEN
  Thesis abstract: During the 17th and 18th centuries, an intense period of construction activity took place in Catalonia. In the case of churches, this was a historical period that produced a large number of architectural interventions. Several studies on buildings constructed during the Catalan Baroque period are currently available, but most have focused primarily on historiographical, compositional, and stylistic aspects. Much research has focused on specific regional areas, and some of it on specific buildings. Consequently, to date, no comprehensive description has been made of the construction techniques commonly used in church buildings throughout the 17th and 18th centuries across the Catalan territory. Through a critical analysis of existing scientific and academic contributions, gaps in knowledge regarding the construction techniques used were identified, leading to a specific study that has enabled a more precise and comprehensive description of these techniques. The research method included compiling an inventory of churches with documented interventions, extracting and thematically classifying the information, and providing a final description of the construction knowledge. As a result of applying this method to the case studies, it has been possible to refine, complete, and expand our understanding of the design and execution of church construction projects by their builders. The existence of a sequential construction process for carrying out the works has been confirmed, based on the mastery of the construction trades by the master builders involved, with a specific repertoire of elements and materials. In this regard, for example, the identification of the construction processes for creating large openings in existing walls, increasing the thickness of existing walls, laying roof tiles, the controlled demolition of stone vault masonry for the reuse of materials, the use of protective surface anti-corrosion primers, or the use of bitumen as a sealing material for masonry joints. It has also been possible to analyze the knowledge documented regarding the conservation, deterioration, and repair of churches. A series of common and recurring pathological processes have been identified, consisting of: moisture and leaks in roofs and walls, cracks and fissures in vaults and walls, and the accumulation of dirt on interior surfaces. The causes of these problems were related to a lack of adequate maintenance, the inherent porosity of the materials and prolonged exposure to atmospheric agents, deficiencies in the foundation and the ground, or a lack of sufficient natural ventilation. Meanwhile, it has been found that the descriptions of the repair processes for these problems are quite vague. In any case, the persistent occurrence of structural damage and deterioration led to the drafting of specific conservation and maintenance instructions for churches. The development of the research also required complementary work to identify the construction terminology specific to this period, revealing the richness of technological content within the analyzed architectural language. In many cases, these are expressions that are now unknown due to the disappearance of the construction processes described, as well as terms that have gradually been replaced by others in line with the natural evolution of languages. Therefore, the results obtained are highly valuable for properly understanding and assessing the construction techniques used in architectural interventions of this period in Catalonia, and as a knowledge base for professionals who must undertake such work now and in the future.
  
• XIE, ZEYUE: Development of nonwoven-reinforced ECCCs and multifunctional smart textiles for energy harvesting and thermal management in buildings
  
  Author: XIE, ZEYUE
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN ARCHITECTURAL, BUILDING CONSTRUCTION AND URBANISM TECHNOLOGY
  Department: Department of Architectural Technology (TA)
  Mode: Normal
  Deposit date: 06/05/2026
  Deposit END date: 19/05/2026
  Thesis director: ARDANUY RASO, MONICA | VENTURA CASELLAS, HEURA
  Thesis abstract: The escalating demand for energy efficiency in modern infrastructure has catalyzed the development of smart building materials capable of active and passive thermal management. While smart textiles and Electrically Conductive Cementitious Composites (ECCCs) offer promising solutions, their practical application is currently hindered by the dispersion limitations of traditional fillers, the mechanical-electrical trade-off, and the lack of multifunctional integration. This thesis addresses these challenges by developing a multifunctional smart fabric and novel nonwoven-reinforced ECCCs, which could culminate in a synergistic, all-weather thermal management system for intelligent building interiors. First, this work addresses the discrepancy in fabric conductivity characterization by establishing a structure-based evaluation framework. Instead of using the same testing method for all materials, contact and non-contact techniques are chosen according to whether the fabric exhibits intrinsic or extrinsic conductivity and its conductivity range, leading to more reliable measurements for different fabric types. Secondly, a multifunctional smart Cotton/Polydopamine/Copper Sulfide/TPU (CO/PDA/CuS/TPU) fabric was fabricated for smart applications. This advanced material integrates anisotropic conductivity, active Joule heating, passive photothermal conversion, and thermoelectric energy harvesting into a single unit. The fabric exhibited rapid active heating (up to 72 °C) and efficient photothermal conversion under solar irradiation. Furthermore, a thermoelectric generator integrated into the fabric demonstrated the capability to harvest energy (350 nW at ΔT = 50 K) and function as a self-powered temperature sensor. Thirdly, a novel ECCC reinforced with stainless steel/polyester (SS/PES) nonwoven fabric was engineered to overcome the agglomeration and loss in mechanical performance issues associated with particulate fillers. The study revealed that the bonding strength of the cement matrix significantly contributes to the generation of the conductive network. An optimal fiber content of 20% was identified near the percolation threshold, which achieved superior electrothermal efficiency compared to higher conductivity formulations (reaching a ΔT of 46.2 °C within 30 minutes at 21 V). The resulting composite demonstrated excellent mechanical properties, durability, and reliable de-icing capabilities. Finally, the functionality of these materials was validated through finite element simulation and experimental testing under varying temperature and humidity conditions. Based on these findings, a closed-loop synergistic ecosystem was proposed, combining the ECCC (as floor heating) and the smart fabric (as curtains). This system dynamically adapts to environmental conditions: utilizing passive photothermal heating and thermoelectric monitoring during the day, and switching to active Joule heating at night, where the curtains recover heat loss from the ECCC elements. This research provides a holistic solution for next generation smart buildings, demonstrating significant potential for energy conservation and intelligent environmental control.
  

DOCTORAL DEGREE IN ARCHITECTURAL, CIVIL AND URBAN HERITAGE AND REFURBISHMENT OF EXISTING BUILDINGS

• PAMIES SAURET, CARLES: Sillerías góticas españolas. Reconstrucción digital mediante fotogrametría de las sillerías góticas españolas: Análisis de la ubicación y funciones del Coro en las Catedrales de los siglos XV-XVI
  
  Author: PAMIES SAURET, CARLES
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN ARCHITECTURAL, CIVIL AND URBAN HERITAGE AND REFURBISHMENT OF EXISTING BUILDINGS
  Department: Departamento de Representación Arquitectónica (RA)
  Mode: Normal
  Deposit date: 11/05/2026
  Deposit END date: 22/05/2026
  Thesis director: NAVARRO DELGADO, ISIDRO | SÁNCHEZ RIERA, ALBERTO
  Thesis abstract: This doctoral thesis examines the layout, evolution, and functions of choirs in Spanish Gothic cathedrals from the 15th and 16th centuries, distinguished by their location in the central nave. Through an interdisciplinary approach combining historical, architectural, and iconographic analysis with advanced photogrammetric techniques, several choir ensembles are digitally documented. The resulting three-dimensional models provide new interpretations of the Spanish choir model, highlighting its liturgical, symbolic, and socio-political significance, while establishing replicable digital methodologies for heritage documentation, preservation, and dissemination.
  

DOCTORAL DEGREE IN ARTIFICIAL INTELLIGENCE

• CAZZARO, FRANCESCO: Advancing Text-to-Query Semantic Parsing Systems
  
  Author: CAZZARO, FRANCESCO
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN ARTIFICIAL INTELLIGENCE
  Department: Department of Computer Science (CS)
  Mode: Normal
  Deposit date: 07/05/2026
  Deposit END date: 20/05/2026
  Thesis director: QUATTONI, ARIADNA JULIETA
  Thesis abstract: Executable Semantic Parsing is the task of mapping a natural language sentence into a formal meaning representation that can be executed over a knowledge base to retrieve information. This task presents several challenges. Natural language variability makes the translation process inherently difficult and, at the same time, meaning representations are highly compositional structures built from elementary units. As a result, semantic parsers must exhibit strong compositional generalization, a capability that remains challenging for current models. Moreover, annotated data for semantic parsing is scarce, and its collection is labor intensive and costly, making it difficult to train models.In this thesis, our aim is to improve and advance semantic parsing systems. We introduce a pipeline that decouples the semantic parsing process into two steps, a translation stage and a reordering stage, which enhances the compositional abilities of the parser. We also propose a data generation method that recombines existing annotated pairs in novel ways. This approach improves the generalization capabilities of semantic parsers while alleviating the data scarcity problem.Furthermore, to address the challenge of limited annotated data, we design a novel approach to automatically generate data pairs from a given knowledge graph without the need of human intervention. This generated data can then be used to train a semantic parsing system specifically designed for that particular knowledge graph. This thesis is among the first to explore semantic parsing for property graphs, where we not only introduce the data generation method but also provide an annotated benchmark for evaluating parsing performance.
  

DOCTORAL DEGREE IN BUSINESS ADMINISTRATION AND MANAGEMENT

• DÍAZ MEDIAVILLA, MARIA ESTELA: Estudio comparativo de la percepción del riesgo laboral entre diferentes colectivos dentro del sector de la construcción
  
  Author: DÍAZ MEDIAVILLA, MARIA ESTELA
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN BUSINESS ADMINISTRATION AND MANAGEMENT
  Department: Department of Management (OE)
  Mode: Normal
  Deposit date: 05/05/2026
  Deposit END date: 18/05/2026
  Thesis director: ABAD PUENTE, JESUS
  Thesis abstract: Objective — The main objective of this study is to empirically evaluate differences in risk perception between construction workers and health and safety managers. Additionally, it aims to determine the effect of work experience and accident experience (both direct and witnessed) on the rational and emotional dimensions of this perception.Methodology — A cross-sectional quantitative design was adopted using a psychometrically validated questionnaire. The final sample consisted of 349 professionals from the construction sector in Catalonia. Data were analyzed using principal component analysis and confirmatory factor analysis to validate the construct structure, followed by multiple linear regression models with interaction terms to test the study hypotheses.Results — The findings empirically demonstrate that, within the analyzed sample, risk perception has a three-dimensional structure composed of rational risk perception, emotional perception of moderate-severity risks, and emotional perception of high-severity risks. A significant safety perception gap was identified: health and safety managers exhibit higher levels of emotional perception regarding high-severity risks compared to workers. In addition, both work experience and accident experience exert different effects on risk perception depending on the dimension analyzed and the professional group considered.Conclusions — Risk perception in the construction sector presents a more complex structure than the traditional two-dimensional conception. The empirical evidence indicates that rational risk perception remains a single dimension, whereas emotional perception is divided into two distinct subdimensions depending on the severity of the potential harm: one related to moderate-severity risks and another associated with high-severity risks. Furthermore, perceptual differences between workers and health and safety managers emerge exclusively in the emotional response to high-severity risks. Finally, experiential factors influence risk perception differently depending on professional role, highlighting the complexity of the cognitive and affective processes involved in risk evaluation in hazardous work environments such as the construction industry.Implications — The findings suggest moving away from “one-size-fits-all” safety approaches. Safety management systems should incorporate training methodologies with a strong emotional impact, such as virtual reality simulations, to reduce workers’ tolerance to risk. For health and safety managers, it is advisable to implement recalibration mechanisms, such as cross-audits and project rotation, to mitigate the overconfidence bias associated with accumulated experience.Limitations — The study has a cross-sectional design, which prevents establishing strict causal relationships in the evolution of risk perception. Furthermore, the data are based on self-reported responses, which may differ from actual on-site perception. Finally, the research context is limited to the construction sector within a specific geographical area, suggesting the need to replicate the model in other high-risk industries.Future Research — Future studies should adopt longitudinal designs to examine how risk perception evolves following exposure to critical events. It would also be valuable to incorporate neurophysiological measurements to capture workers’ immediate emotional responses to risk. Additionally, future research could examine perceptual mimicry within work teams in order to better understand how organizational culture shapes individual risk tolerance.
  

DOCTORAL DEGREE IN CIVIL ENGINEERING

• APARICIO URIBE, CARLOS HUMBERTO: EXPERIMENTAL AND NUMERICAL ANALYSIS OF FLOOD-RELATED HAZARD ASSOCIATED WITH THE EVACUATION OF UNDERGROUND STAIRS
  
  Author: APARICIO URIBE, CARLOS HUMBERTO
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN CIVIL ENGINEERING
  Department: Barcelona School of Civil Engineering (ETSECCPB)
  Mode: Article-based thesis
  Deposit date: 05/05/2026
  Deposit END date: 18/05/2026
  Thesis director: RUSSO, BENIAMINO | TELLEZ ALVAREZ, JACKSON DAVID
  Thesis abstract: The rapid pace of urbanisation has led to the widespread development of underground infrastructure worldwide. Currently, these infrastructures have become increasingly vulnerable to climate-induced extreme weather events such as flooding, particularly in metro stations. Given the unique characteristics of urban underground clusters, stairs serve a dual and critical role during emergencies: they are often the primary evacuation route while simultaneously serving as the main entry path for the incoming floodwaters. This doctoral research explores the interaction between hydrodynamic forces and human evacuation on flooded stairs within underground environments. The study aims to achieve a better understanding of these interactions to identify stability thresholds, establish safety criteria, and develop effective evacuation strategies. A comprehensive approach to the problem was adopted by integrating both experimental and numerical methods.A thorough state-of-the-art review was conducted to identify the current knowledge landscape, highlighting key findings, limitations and potential further exploratory lines. The experimental component of this thesis was based on a full-scale replica constructed at the hydraulic laboratory of the School of Civil Engineering at the Universitat Politècnica de Catalunya – BarcelonaTech (UPC). This represented the flood-prone access stairs of the Paral·lel metro station, which is part of Barcelona’s Metro Network. The stairs’ hydraulic characterisation was followed by a series of experimental tests involving individual subjects walking the flooded stairs under varying discharge conditions and footwear types. Additional tests were assessed under the facility’s maximum discharge, with subjects simultaneously walking the flooded stairs in groups of two and three. A first individual subject's campaign aimed to identify thresholds for pedestrian instability, while a second one intended to optimise evacuation based on the different studied grouping configurations.To complement the experimental observations, several three-dimensional Computational Fluid Dynamics (3D-CFD) simulations were performed using the commercial software FLOW-3D. These simulations analysed the water-dragging force considering single subjects and pedestrian arrangements, providing a detailed identification of hydraulic parameters and water forces throughout the stairs’ domain. This research offers novel insights into flood risk management in urban areas, taking into account their site-specific constraints, particularly access to underground spaces via stairs. Special attention is given to subjects evacuating simultaneously, as well as optimal strategies for managing group evacuations. The outcomes offer valuable, actionable data for engineers, urban planners, and emergency responders. In this way, it will ultimately contribute to improving evacuation safety and hydraulic understanding of flooded underground stairs through the integration of experimental and numerical analysis, thereby fostering urban resilience and evacuation planning in flood-prone underground infrastructures.
  

DOCTORAL DEGREE IN COMPUTER ARCHITECTURE

• DE LIMAS SANTANA, ALEXANDRE: Beyond 512-bit vectors: Optimized and performance portable AI operators on vector-length-agnostic architectures
  
  Author: DE LIMAS SANTANA, ALEXANDRE
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN COMPUTER ARCHITECTURE
  Department: Department of Computer Architecture (DAC)
  Mode: Normal
  Deposit date: 11/05/2026
  Deposit END date: 22/05/2026
  Thesis director: CASAS GUIX, MARC | ARMEJACH SANOSA, ADRIÀ
  Thesis abstract: This thesis addresses the challenges of producing high-performance, portable code for arithmetic-intensive operations in Deep Neural Network (DNN) workloads, such as convolutions and matrix multiplications.It targets emerging vector and matrix architectures exposing software interfaces to program accelerators via Instruction Set Architecture (ISA) extensions.It proposes techniques to handle three main challenges related to accelerating DNN workloads on contemporary data-parallel CPU architectures: i) extrapolating software optimization techniques designed primarily for high-end general-purpose vector processors to also accomodate edge devices and long vector accelerators hardware, ii) providing performance portability for open standard vector ISA ecosystems, like RISC-V, characterized by their unprecedent micro-architectural discrepancy among implementations, and iii) designing matrix multiplication ISA extensions that uphold the core principles of emerging vector architecture concerning implementation flexibility, such as vector length agnosticism.This thesis presents empirical evidence that existing software optimization techniques for generating high-performance implementations of DNN operators for modern vector processors are biased toward high-end general-purpose systems with 512-bit vectors (e.g., Intel Cascade Lake, Fugaku AF64X).The techniques presented in the thesis support the idea of hardware/software co-design and the need to consider microarchitectural features, such as vector length, non-conventional memory subsystems, and the presence/lack of out-of-order pipelines, when generating code for vector and matrix processors.Specifically, this thesis employs runtime specialization to adapt established DNN algorithms to a broader range of processor designs, equipping applications with flexible code generators that dynamically optimize the algorithms for the specific combination of the platform's microarchitectural features and the operation's hyperparameters. The thesis makes three key contributions.First, it provides the first performance analysis of convolution workloads on a 16,384-bit vector processor, identifies cache conflict misses in existing techniques, and proposes software corrections that yield up to 1.83x speedups over prior approaches.Second, it introduces a dynamic matrix multiplication and convolution code generator that adapts algorithmic optimization techniques, such as register unrolling, to the vector width of the target platform.A performance analysis of three commercially available RISC-V systems running computer vision workloads shows geometric speedups ranging from 1.43x to 3.58x compared to state-of-the-art libraries.Third, it proposes a Matrix Tile Extension (MTE) to supplement vector-length-agnostic ISAs with geometry-agnostic matrix multiplication and memory operations.Two microarchitectures supporting this extension are described: a lean extension of an 8192-bit vector processor and a systolic-array-based design, both of which utilize vector registers for matrix storage and demonstrate 1.35x geometric mean speedups on GEMM and convolution workloads.
  
• SHAKESPEAR MILES, HAILEY JOSEPHINE: Multi-Agent Systems for Optical Networks
  
  Author: SHAKESPEAR MILES, HAILEY JOSEPHINE
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN COMPUTER ARCHITECTURE
  Department: Department of Computer Architecture (DAC)
  Mode: Normal
  Deposit date: 15/05/2026
  Deposit END date: 29/05/2026
  Thesis director: VELASCO ESTEBAN, LUIS DOMINGO | BARZEGAR, SIMA
  Thesis abstract: Unlike earlier mobile generations, 6G is expected to support a wide range of applications such as immersive communications, remote healthcare, autonomous transportation, and smart cities. These use cases will significantly increase the number of connected devices and impose stringent requirements on bandwidth, latency, reliability, and energy efficiency. As a result, the networks supporting these services will face major challenges in scalability, resource management, and control. In this context, this doctoral thesis investigates the use of Multi-Agent Systems (MAS) as a foundation for next-generation network control. The goal of this thesis is to design and evaluate MAS-based solutions that improve the intelligence, scalability, and energy efficiency of optical networks across both the optical and packet layers.The first objective addresses the optical layer by investigating centralized and distributed MAS-based approaches for dynamic spectrum control in point-to-multipoint (P2MP) connections. A centralized solution based on traffic prediction and integer linear programming computes optimal allocations under near-real-time constraints, achieving high spectrum utilization but introducing synchronization and scalability limitations. To overcome these issues, distributed architectures are proposed in which transponder agents perform decision-making locally. Three strategies are studied: a mixed-strategy gaming model, a distributed deterministic algorithm, and a multi-agent reinforcement learning (MARL) approach. The MARL solution achieves the best overall performance by anticipating traffic variations and allocating capacity proactively, while distributed methods significantly improve scalability and robustness. Communication efficiency is also studied with the MARL approach allowing for asynchronous operation and reducing inter-agent messaging. Results show that distributed MAS can approach centralized performance while avoiding bottlenecks and single points of failure.The second objective focuses on the packet layer, where an extended MAS architecture enables end-to-end near-real-time control of network services (NS) through autonomous flow operation. Routing decisions are driven by telemetry and optimized using Deep Reinforcement Learning (DRL) to minimize delay and operational cost, while agents monitor performance and coordinate with the software-defined networking (SDN) controller. The architecture supports the full lifecycle of a NS, including deployment, dynamic reconfiguration, and handover scenarios. A model-selection approach based on offline training and real-time telemetry is proposed, together with an active probe-testing mechanism and long short-term memory (LSTM) based traffic prediction trained online by flow agents. Simulations demonstrate that transferring of trained models between agents enables accurate predictions and knowledge generation allowing for fast reconfiguration decisions while maintaining QoS over the NS. This MAS architecture provides the foundation for the third objective where experimental results demonstrate reliable QoS maintenance and effective MAS reconfiguration during operation.In conclusion, this thesis shows that MAS combined with learning-based decision-making, predictive analytics, and distributed control provide a flexible and effective framework for managing future networks. The proposed solutions improve scalability, adaptability, and energy efficiency while maintaining strict performance guarantees, establishing MAS as a key enabler for intelligent and autonomous 6G networks.
  

DOCTORAL DEGREE IN EARTHQUAKE ENGINEERING AND STRUCTURAL DYNAMICS

• MOMIN, SAMAR: An AI-Augmented Scalable Framework for High-Resolution Exposure and Seismic Risk Assessment
  
  Author: MOMIN, SAMAR
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN EARTHQUAKE ENGINEERING AND STRUCTURAL DYNAMICS
  Department: Department of Civil and Environmental Engineering (DECA)
  Mode: Normal
  Deposit date: 13/05/2026
  Deposit END date: 27/05/2026
  Thesis director: CARREÑO TIBADUIZA, MARTHA LILIANA
  Thesis abstract: Large-scale high-resolution probabilistic seismic and multi-hazard risk assessments depend critically on how exposure is represented and how appropriately vulnerability functions are assigned to heterogeneous building stocks. While hazard modelling has achieved a high degree of harmonisation and vulnerability modelling has achieved a high level of computational maturity, the exposure–vulnerability interface remains manual, typology-driven and difficult to scale consistently. In Barcelona and across Catalonia, vulnerability functions derived from single buildings or limited samples have frequently been extrapolated across portfolios using coarse classifications. When hazard, exposure and damage-to-loss models are held constant, this practice produces substantial spreads in portfolio loss metrics. It first quantifies that variability through a controlled vulnerability-only sensitivity analysis for Barcelona. Using a consistent probabilistic seismic hazard backbone and unified damage-to-loss mapping, published vulnerability-function sets are interchanged while all other components remain fixed. Results show that Average Annual Loss (AAL) varies from approximately €25.6 million to €348.0 million depending solely on the selected vulnerability dataset, demonstrating order-of-magnitude sensitivity attributable to function assignment. A focused pilot in the Eixample district confirms that detailed, manual reassignment (e.g., corner versus centre block positions) improves local realism but does not scale to city- or regional-level portfolios.To address this structural limitation, the second step moves from ad-hoc assignment toward repeatable and transparent exposure. The thesis develops a geometry-driven exposure structuring workflow through Building Footprint Analysis, Classification and Grouping (BFA/BFC/BFG). High-resolution cadastral building footprints from Catalonia are used to extract dimensions and are transformed into interpretable, reproducible plan-shape classes using previously computed shape metrics to define sides-based classes. Results from comparative analysis of several shape metrics show that simple compactness measures agree on regular rectangles and squares but blur important differences for elongated, irregular and opening-based plans. This reveals redundancy among scalar compactness indices and limited discriminatory power for articulated and opening-based geometries, highlighting the need for an alternative capable of preserving fine-grained distinctions relevant to structural behaviour.The Building Footprint Analysis, Recognition and Classification framework (BFARC-YOLO) builds on this basis to enable scalable AI-augmented exposure classification. Approximately 1.8 million building footprints are rendered as oriented silhouettes and progressively trained using ~0.34% labelled samples. The final model achieves mean average precision (mAP@0.5:0.95) ≈ 0.96 across 44 fine-grained classes. GPU optimisation reduces regional inference time from over 490 hours (CPU baseline) to ~72 hours, demonstrating operational feasibility. Interoperability with the GEM taxonomy enables direct integration into established risk platforms and a lightweight web interface supports auditable batch inference.Rather than developing novel fragility functions, this thesis offers modelling infrastructure. It creates a replicable bridge from cadastral geometry to vulnerability-aligned exposure classification, minimising subjectivity in vulnerability assignment and allowing for scalable, transparent portfolio risk modelling. By reframing exposure as a structured, learning-enabled modelling layer, the study lays the groundwork for future fragility calibration, and next-generation multi-hazard risk assessments for dense and heterogeneous cities, regions, countries and the global scale.
  

DOCTORAL DEGREE IN ELECTRONIC ENGINEERING

• HERNÁNDEZ URREA, MARC: Design and Implementation of Novel Multiparametric Nonclinical Cardiovascular Assessment Devices Using Only Four Electrodes
  
  Author: HERNÁNDEZ URREA, MARC
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN ELECTRONIC ENGINEERING
  Department: Department of Electronic Engineering (EEL)
  Mode: Normal
  Deposit date: 08/05/2026
  Deposit END date: 21/05/2026
  Thesis director: CASAS PIEDRAFITA, JAIME OSCAR | CASANELLA ALONSO, RAMON
  Thesis abstract: Cardiovascular diseases remain the leading cause of adverse outcomes and healthcare burden worldwide. The development of extra-hospital cardiovascular (CV) monitoring strategies has become increasingly important, especially after the COVID-19 pandemic exposed healthcare system limitations and the need for alternative screening and monitoring approaches to reduce hospital congestion. Organizations such as the World Health Organization (WHO) highlight that nonclinical devices for cardiovascular disease (CVD) assessment improve treatment adherence, rehabilitation outcomes, survival indices, and contribute to more sustainable healthcare systems. These technologies have also attracted interest from the pharmaceutical industry, as home-based clinical trials and remote patient monitoring provide advantages over traditional trials, including improved adherence, supervision, dose control, and cohort management.Currently, most nonclinical devices rely on electrocardiogram (ECG) or photoplethysmogram (PPG). ECG provides waveform information, time intervals, heart rate (HR), and heart rate variability (HRV), but only electrical heart information. PPG provides arterial pulse wave (APW) morphology, pulse rate (PR), oxygen saturation, and sometimes blood pressure from distal measurements, but only mechanical information limited to superficial arteries. Other systems for deep artery diagnosis, such as SphygmoCor, echocardiography, and cardiac catheterization, remain restricted to clinical environments due to complexity, cost, and need for skilled personnel. Similarly, impedance plethysmogram systems such as impedance cardiography are limited to clinical use, while nonclinical impedance applications are usually restricted to single limbs. Recent advances show that impedance plethysmogram (IPG) measured between limbs can provide information about APW propagation times proximal to the aorta.This thesis presents the design and validation of a novel, easy-to-use cardiovascular assessment device based on simultaneous acquisition of ECG, multiple IPG signals (limb-to-limb and local), and ballistocardiogram (BCG) using only four electrodes in contact with hands or feet and a home weighing scale, together with algorithms to extract cardiovascular health indicators. The device provides information from time intervals between ECG and IPG signals, such as pulse arrival time (PAT), including proximal information from limb-to-limb IPG and distal information from local IPG measurements. These measurements are combined to estimate pulse transit time (PTT), related to arterial elasticity. The combination of hand-to-hand and foot-to-foot IPG PAT enables estimation of a surrogate of aortic PTT (aPTT), typically obtained from carotid–femoral PTT (cf-PTT) using tonometry, a marker associated with ageing and cardiovascular health. The device also enables respiration extraction from limb-to-limb IPG signals without additional sensors.The system was validated through measurement campaigns using reference instruments such as impedance cardiography and tonometry. The devices showed adequate signal-to-noise ratio (SNR: 41 dB and 22 dB) and acceptable CMRR (55 dB and 25 dB). Comparative studies showed good agreement with impedance plethysmography (r > 0.90). Tonometric PAT and PTT estimations agreed with IPG-derived parameters. Wrist-to-wrist IPG PAT correlated with carotid PAT (r = 0.85), and foot-to-foot IPG PAT with femoral PAT (r = 0.86). IPG-derived carotid–femoral PTT showed moderate correlation with tonometer-based cf-PTT (r = 0.67).These results validate the proposed instrumentation and methodology, demonstrating reliable extraction of clinically relevant cardiovascular timing parameters. Overall, a compact system combining ECG and limb-to-limb IPG enables acquisition of relevant proximal and distal arterial information for nonclinical cardiovascular assessment.
  

DOCTORAL DEGREE IN ENGINEERING, SCIENCES AND TECHNOLOGY EDUCATION

• JULIAN TRUJILLO, EDWIN CRISTIAN: Modelización Flexible y actividad matemática en Ingeniería: análisis de la interpretación, simulación y validación de sistemas dinámicos no lineales
  
  Author: JULIAN TRUJILLO, EDWIN CRISTIAN
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN ENGINEERING, SCIENCES AND TECHNOLOGY EDUCATION
  Department: Institute of Education Sciences (ICE)
  Mode: Normal
  Deposit date: 12/05/2026
  Deposit END date: 26/05/2026
  Thesis director: GOMEZ URGELLES, JOAN VICENÇ
  Thesis abstract: The teaching of differential equations in engineering remains marked by a dissociation between procedural rigor and the need to interpret, simulate, and validate dynamic systems in professional settings. This dissertation examines how a Flexible Modeling approach transforms the mathematical activity of engineering students when their work with differential equations shifts away from isolated solution procedures toward exploration, comparison, and model-based justification. Adopting a situated, longitudinal, and mixed-methods design, the study articulated didactic units, computational laboratories, and discipline-based projects, which were analyzed through the triangulation of academic products, artificial intelligence auditing, laboratory work, and technical defenses. The analysis reveals a consistent shift toward forms of mathematical activity centered on parametric sensitivity, dynamic system interpretation, and the technical validation of results. In particular, simulation functioned as an environment for epistemic contrast, while the auditing of automated responses strengthened students’ ability to question results that were inconsistent with the Jacobian, the phase plane, and the physical plausibility of the phenomenon. The dissertation concludes that Flexible Modeling shifts the focus of instruction from algorithmic execution toward interpretation, validation, and technical justification, although this process shows uneven appropriation depending on students’ prior mathematical background. In this sense, mathematics is repositioned as a language of diagnosis and auditing in the age of automation
  

DOCTORAL DEGREE IN ENVIRONMENTAL ENGINEERING

• CUESTA I MOTA, DÍDAC: Desenvolupament de tecnologies electroquímiques per a l’obtenció d’hidrogen en el tractament d’efluents industrials tèxtils
  
  Author: CUESTA I MOTA, DÍDAC
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN ENVIRONMENTAL ENGINEERING
  Department: Department of Civil and Environmental Engineering (DECA)
  Mode: Normal
  Deposit date: 12/05/2026
  Deposit END date: 26/05/2026
  Thesis director: LOPEZ GRIMAU, VICTOR | CANALS CASALS, LLUC
  Thesis abstract: In the current context of climate emergency and water stress, the textile industry faces the challenge of reducing its high water consumption and its greenhouse gas emissions. This thesis investigates the feasibility of an innovative electrochemical system capable of simultaneously performing the purification of complex waste effluents and the production of hydrogen, thus integrating wastewater treatment with the generation of an energy vector without direct CO2 emissions.The study identifies dye effluents with reactive dyes and mercerizing effluents as the most suitable for the integration of a dual system, thanks to their high conductivity and alkalinity. While mercerizing effluents act as an ideal electrolyte for alkaline electrolysis due to their 20% NaOH content, dye effluents contain recalcitrant azoic reactive dyes that cannot be removed by conventional biological methods, but are highly sensitive to electrochemical oxidation.One of the pillars of the thesis is the design and validation of a sandwich-type bi-compartmentalized electrochemical cell with an anion exchange membrane (AEM). This configuration allows separating the waste effluent at the anode from the alkaline electrolyte at the cathode, avoiding hydrogen contamination and improving the energy efficiency of the treatment. Experimental results demonstrate a decolorization efficiency higher than 99% for diverse reactive dyes and hydrogen generation with a purity of 98,7%. The optimal configuration that balances energy efficiency, treatment time, and material cost for the treatment is also determined, with a current density of 150 mA/cm2 and a combination of a Ni cathode and an Ir-Ru/MMO or BDD anode depending on whether the effluent contains NaCl or Na2SO4.Furthermore, a computer model has been developed using the eCherry library (Modelica) that allows predicting the behavior of the system with a maximum error of 5% in the expected decolorization and 2,1% in the working voltage.In conclusion, the work demonstrates that electrochemical treatment is not only an effective solution for the decolorization of dye effluents but also becomes a strategic pathway for the circular economy, allowing the energy recovery of hydrogen through blending in boilers, and opening the door to the reuse of water, salts, and alkali.
  

DOCTORAL DEGREE IN GEOTECHNICAL ENGINEERING

• ALHARFOUCH, LOUJAIN: Integrating ecohydrological, isotopic, and numerical approaches to assess water use by montane Scots pine under varying wetness conditions
  
  Author: ALHARFOUCH, LOUJAIN
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN GEOTECHNICAL ENGINEERING
  Department: Department of Civil and Environmental Engineering (DECA)
  Mode: Normal
  Deposit date: 14/05/2026
  Deposit END date: 28/05/2026
  Thesis director: HIDALGO GONZÁLEZ, JUAN JOSÉ | LATRON, JÉRÔME
  Thesis abstract: Ecohydrology provides a critical framework for understanding interactions between vegetation and the hydrological cycle, particularly in forested ecosystems that strongly regulate water fluxes and ecosystem resilience. In Mediterranean mountain regions, increasing climate variability and recurrent droughts place forests close to their hydraulic limits. Despite these mounting pressures, key uncertainties remain regarding how trees access, store, and utilize water across contrasting wetness conditions, and how these processes can be represented in ecohydrological models.This thesis investigates tree water uptake dynamics and their representation by integrating high temporal- and spatial-resolution field observations, stable water isotopes, and process-based modeling. The study was conducted in the Vallcebre Research Catchments (NE Spain), focusing on Scots pine (Pinus sylvestris L.). Field observations combined meteorological measurements, sap flow and stem diameter variations, soil water content and potential, groundwater levels, and weekly water stable isotopes sampling of precipitation, soil water, groundwater, and xylem water. These data were analyzed using statistical approaches and numerical modeling to examine tree water uptake at the plot scale during contrasting dry and wet periods.Field-based ecohydrological and isotopic analyses revealed that Scots pine water use is strongly constrained by soil water potential during drought, with trees relying on internal stem water storage and showing limited coupling to recent precipitation inputs. Stable isotope evidence demonstrated a dominant contribution of winter-recharged soil water stored in tightly bound soil pores, even following intense summer rainfall events. Only after sustained soil rewetting did trees begin incorporating summer precipitation, ultimately sourcing water from a mixture of winter and summer inputs. These findings indicate a clear preference for stable water pools with longer residence times and highlight the critical role of winter precipitation in sustaining tree hydraulic functioning in Mediterranean mountain forests.Building on these plot-scale observations, the second part of the thesis develops a physically grounded, calibration-free root water uptake parameterization based on soil moisture drying rates and constrained by sap-flow-derived transpiration. When implemented in a numerical model, this approach consistently outperformed commonly used empirical root distribution functions in reproducing soil water content and soil water potential dynamics. Independent validation using stable water isotopes confirmed the robustness of the proposed modeling framework and revealed tree water-use patterns under dry and wet conditions that were consistent with the field-based ecohydrological and isotopic findings.Agreement between field-based and modeling-based findings demonstrates that integrating ecohydrological observations, stable isotopic tracers, and physically grounded modeling provides a coherent and robust framework for advancing understanding of tree water uptake. The research in this thesis contributes to improving the realism of root water uptake representations in ecohydrological models and emphasizes the importance of stored winter precipitation for forest resilience in water-limited environments.
  

DOCTORAL DEGREE IN MARINE SCIENCES

• CARRION BERTRAN, NIL: Influence of topobathymetric and offshore forcing uncertainties on beach morphodynamic predictability
  
  Author: CARRION BERTRAN, NIL
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN MARINE SCIENCES
  Department: Department of Civil and Environmental Engineering (DECA)
  Mode: Normal
  Deposit date: 11/05/2026
  Deposit END date: 22/05/2026
  Thesis director: CALVETE MANRIQUE, DANIEL | RIBAS PRATS, FRANCESCA
  Thesis abstract: Sandy beaches are among the most important, yet most vulnerable, environments on Earth. These regions are home to over 30% of the world's population and host a vast array of socioeconomic activities. As the interface between sea and land, they are highly dynamic systems where numerous processes occur at different temporal and spatial scales, continuously reshaping the morphology of the coast. Furthermore, climate change could modify these processes, producing not only long-term effects but also, in the short and medium term, impacts that significantly affect beach morphology.In order to investigate these effects, morphodynamic numerical models are key tools, allowing the analysis of past and present conditions, as well as to explore future scenarios. However, their application involves several sources of uncertainty. This thesis focuses primarily on uncertainties related to offshore forcing and initial topobathymetric conditions across multiple temporal and spatial scales.In the short term, the process-based XBeach model was employed to develop a conceptual model of how the initial topobathymetry influences the generation of washover deposits on a synthetic beach inspired by Castelldefels data (Spain) during a storm event. The study highlighted the importance of using detailed initial topobathymetries that incorporate potential morphological patterns to accurately analyse washover deposit generation. In particular, the inclusion of morphological patterns with varying wavelengths was shown to significantly affect hydrodynamic behaviour and sediment transport patterns, thereby influencing the distribution, extent, and volume of washover deposits. For instance, the presence of megacusps of certain wavelengths could enhance washover deposition by up to a factor four compared to scenarios in which such patterns were not included. This study proved the significant potential modelling uncertainties linked to unknowns not only in the initial topobathymetry but also in the incident wave group chronology, which can be included in this model, and may have a stronger influence on washover deposit formation than variations in the initial cross-shore profile. In the medium term, both the process-based XBeach model and the reduced-complexity Q2Dmorfo model were used to investigate how different hydrodynamic forcing sources, including waves and sea level, affect the modelling of the embayed Castell Beach (Palamós, Spain) over a six-month period. The study demonstrated that inaccurate representations of wave conditions can significantly affect the modelling of an embayed beach in the medium term, whilst different sea level sources produced similar results. In particular, biases in wave direction from hindcast data resulted in poor representations of shoreline evolution, although the model could recover if hindcast data was only employed for only a couple of months. These results highlight the uncertainties related with the selection of forcing sources to simulate the evolution of an embayed beach.In the long term, the Q2Dmorfo model was employed to investigate the influence of wave storm chronology on the projected evolution of the Llobregat Delta up to 2100. The study highlighted the strong dependence of the model response on the initial adaptation period, which appeared to be influenced by the wave chronology. However, once the model reached a quasi-equilibrium state during this adaptation phase, the subsequent low dynamism, resulting from the model calibration, was primarily controlled by the resulting topobathymetry rather than by the wave chronology. Consequently, the multiple realisations performed converged towards a similar morphological response up to 2100, limiting the ability to assess the role of storm chronology in long-term shoreline evolution. To address this limitation, several methodological improvements are proposed, with the objective of capturing the potential role of wave chronology in further work.
  

DOCTORAL DEGREE IN MECHANICAL, FLUIDS AND AEROSPACE ENGINEERING

• MASCLANS SERRAT, NÚRIA: Scientific Machine Learning in Turbulent Flows: Observability, Reconstruction & Acceleration
  
  Author: MASCLANS SERRAT, NÚRIA
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN MECHANICAL, FLUIDS AND AEROSPACE ENGINEERING
  Department: Department of Mechanical Engineering (EM)
  Mode: Normal
  Deposit date: 06/05/2026
  Deposit END date: 19/05/2026
  Thesis director: JOFRE CRUANYES, LLUÍS
  Thesis abstract: The analysis and design of engineering systems governed by wall-bounded turbulent supercritical fluid flows are fundamentally constrained by two distinct barriers: the intrinsic limitations of optical diagnostics in resolving scalar thermodynamic fields under extreme fluid regimes, and the prohibitive computational cost required to achieve fully converged flow statistics in high-fidelity simulations. This thesis addresses these challenges by developing advanced scientific machine learning (SciML) frameworks that rigorously integrate physical domain knowledge into deep learning architectures.To overcome the experimental observability gap inherent to high-pressure transcritical flows, this work first proposes a novel thermophysics-informed neural network (TINN). By embedding the real-gas equation of state directly into the network's optimization loss function as a soft constraint, while enforcing physical boundary conditions through the hard-constrained network architecture, this framework successfully reconstructs hidden thermodynamic state variables, specifically density and temperature, exclusively from available kinematic velocity data. This methodology provides a reliable, non-intrusive alternative to overcome the severe optical distortions that traditionally limit quantitative scalar measurements in supercritical fluid experiments. To address the computational burden of temporal numerical integration, the thesis introduces a paradigm shift in turbulence simulation by adapting deep reinforcement learning (DRL) to accelerate the convergence of flow statistics. Specifically, a Reynolds eigenspace perturbation (REP)-based DRL methodology is formulated. In this approach, a distributed multi-agent DRL framework acts as an active flow control system, iteratively interacting with the numerical solver to optimize the instantaneous flow trajectory. Initially established and validated on a reduced one-dimensional turbulence (ODT) model, the agents apply mathematically constrained perturbations directly to the eigenspace of the Reynolds stress tensor. This strict structural constraint ensures that all dynamic modifications to the Reynolds stress magnitude, shape, and orientation maintain rigorous physical realizability. This hybrid DRL-CFD methodology is subsequently scaled to fully resolved, three-dimensional direct numerical simulations (DNS) of turbulent channel flows. By overcoming complex software engineering barriers to achieve a low-latency, in-memory coupling between the dynamic Python-based REP-DRL framework and a massively parallelized C++ CFD solver, the implementation dynamically manipulates the instantaneous flow fields. While the CFD simulation operates within a statistically stationary state, this active flow control framework steers the system to achieve statistical convergence in a significantly reduced integration time.Collectively, this thesis demonstrates that embedding physical laws, thermodynamic equations, and structural constraints into machine learning algorithms transforms them from passive data interpolators into scalable, physically consistent frameworks capable of both recovering hidden flow physics and actively accelerating numerical simulations. The proposed methodologies establish a foundational pathway towards bridging the gap between experimental measurement limitations and computational feasibility, thereby facilitating both the fundamental study and the practical engineering design of complex turbulent flows.
  

DOCTORAL DEGREE IN OPTICAL ENGINEERING

• RAMÍREZ CANO, NATALIA: Medida morfológica de la tortuosidad de los vasos retinianos mediante análisis digital de imagen
  
  Author: RAMÍREZ CANO, NATALIA
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN OPTICAL ENGINEERING
  Department: Department of Optics and Optometry (OO)
  Mode: Article-based thesis
  Deposit date: 07/05/2026
  Deposit END date: 20/05/2026
  Thesis director: MILLAN GARCIA VARELA, MARIA SAGRARIO
  Thesis abstract: Retinal vascular tortuosity has been widely identified by the medical community as a direct and non-invasive biomarker for the development of ocular, vascular, and systemic diseases. This is because the retinal vascular network is a delicate structure highly sensitive to alterations in blood pressure and other hemodynamic factors. These alterations produce observable changes in its vessels, modifying morphological parameters such as thickness, curvature, deviation, and tortuosity level. This makes the retinal vascular network a prime target organ for the early detection of ocular and vascular pathologies.Quantitative analysis of tortuosity in the retinal vascular network is typically performed by processing digital retinographies using algorithms that allow for the assessment of tortuosity indices. Local (characterizing the tortuosity of vessel segments free of bifurcations) and global (synthesizing the tortuosity information of the entire vascular network) indices are used. However, the calculation of these indices is conditioned by technical factors such as the spatial resolution of the images, the framing used in the acquisition of the retinographies (centered on the macula or the optic disc), as well as the choice of combinations of weighting schemes and local indices for calculating the global tortuosity indices.This doctoral thesis, presented as a compilation of three scientific publications, comprehensively addresses these challenges. First, it studies the impact of the retinographies’ framing (macula or optic disc) on the estimation of local tortuosity. Second, it evaluates the influence of image resolution on the robustness of the calculation of the local indices. Finally, an objective and quantitative validation framework for global indices based on the mathematical composition of local tortuosity index measures is proposed.The results obtained allow for the establishment of usage criteria for the selection of reliable local indices, demonstrate the limitations arising from acquisition conditions, and propose a methodological bridge between local and global measures. Thus, the thesis contributes to the standardization of tortuosity analysis in retinal images, with the aim of improving its clinical applicability and its value as a biomarker for systemic diseases.
  

DOCTORAL DEGREE IN PHOTONICS

• MORENO ABAJO, ÁLVARO: Optical investigation of 2D materials with in-plane engineering: exciton confinement and chirality sensing
  
  Author: MORENO ABAJO, ÁLVARO
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN PHOTONICS
  Department: Institute of Photonic Sciences (ICFO)
  Mode: Normal
  Deposit date: 11/05/2026
  Deposit END date: 22/05/2026
  Thesis director: KOPPENS, FRANK | RESERBAT-PLANTEY, ANTOINE
  Thesis abstract: Two-dimensional (2D) materials provide a powerful platform for nanoscale engineering and control of geometry and energy landscapes without directly modifying the material, owing to their interfacial nature. This thesis explores how we can harness this potential by introducing in-plane engineering in van der Waals heterostructures. We present two case studies: in one, the exciton dimensionality is reduced using a designed 1D electrostatic trap; in the other, twisting two monolayers yields a chiral configuration that modifies the interaction with other chiral systems. Together, these two parts demonstrate how inplane symmetry breaking (translational and mirror symmetry, respectively) in 2D material platforms enables new modalities of control and sensing.In the first part, we investigate electrostatically defined confinement of intralayer excitons in MoSe2. A p–i–n junction is induced in the monolayer by asymmetric gating, creating a tight 1D potential with an effective exciton confinement length down to 10nm. Combining photoluminescence and reflectance-contrast spectroscopy, we resolve a discrete spectrum of localized states arising from center-of-mass quantization, with linear polarization aligned with the trap geometry, consistent with confinement-enhanced valley-exchange interactions. Importantly for the development of this technique, we show that the confinement potential cannot be understood as a purely electrostatic effect. Illumination reshapes device operation by inducing dissociation-driven photo-doping and Auger-assisted charge extraction, thereby stabilizing a working p–i–n configuration. We resolve photoinduced carrier-redistribution dynamics on the scale of seconds and demonstrate that their dependence on excitation position produces sharp switching between confined and unconfined excitonic responses. A rate-equation description captures the competition between dissociation and Auger processes, highlighting a route to nonlocal optical control of carrier density and, consequently, of the confinement potential. Programmable excitonic potentials that reach the 0D limit could enable quantum technologies such as single-photon sources or optically addressable qubits, and open a route toward strong exciton–exciton interactions and Bose–Hubbard physics.In the second part, we leverage the structural chirality of twisted bilayer graphene (TBG) to realize a novel enantiomeric sensing strategy based on chirality-dependent non-radiative energy transfer. In the presence of TBG, the decay rate of chiral fluorophores is modified depending on handedness matching between molecule and substrate, which we read out by measuring the fluorescence lifetime in time-resolved photoluminescence experiments. The observed asymmetry is statistically tested by spatially resolving the enantioselective contrast, observing a sign reversal upon inversion of the TBG handedness, and exploring the role of the twist angle as a control parameter. We quantify the effect of chirality through a lifetime-based dissymmetry factor that reaches the 1 – 10% level, implying an enhancement of several orders of magnitude compared with the natural optical circular dichroism of both the molecule and TBG. The presented approach is conceptually distinct from schemes that rely on electromagnetic field engineering, and achieves sensitivities down to the single-molecule layer without requiring surface functionalization. This opens the door to developing a platform with tunable, strong chiral light–matter interactions with implications in optics, sensing, and chemistry, including chiral catalysis and homochiral synthesis.
  
• NIMJE, KARTIKA NARAYAN: Photonic Strategies for Approaching Fundamental Limits in Thermophotovoltaic Energy Conversion
  
  Author: NIMJE, KARTIKA NARAYAN
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN PHOTONICS
  Department: Institute of Photonic Sciences (ICFO)
  Mode: Normal
  Deposit date: 06/05/2026
  Deposit END date: 19/05/2026
  Thesis director: PAPADAKI, GEORGIA THENO
  Thesis abstract: Thermophotovoltaic (TPV) systems convert thermal radiation into electricity by coupling a hot emitter to a photovoltaic (PV) cell. Despite important recent developments in the field, TPV performance is ultimately constrained by a persistent power--efficiency trade-off: broadband radiative exchange between an emitter and a cell yields high electrical power at the expense of efficiency, whereas narrowband exchange leads to high efficiency at the cost of reduced power. A more empirical way to express this constraint is the difficulty of improving both the current and voltage characteristics of a cell simultaneously. Understanding the power--efficiency trade-off is therefore fundamental to optimizing TPV performance. This thesis develops a unified framework for understanding this trade-off and identifying photonic and electronic design strategies that enable optimal operation while remaining anchored to thermodynamic bounds.The first part of the thesis establishes the conceptual and quantitative backbone. A photonic view of thermal radiation connects Planck’s law and the Stefan--Boltzmann limit to the density of optical states, coherence, and surface polaritons, and clarifies the distinction between far-field and near-field exchange. These ideas are combined with the practical building blocks of TPVs — spectral and angular emission control, junction physics, recombination, and quantum efficiency — to establish a practical design toolkit for TPVs. Within this toolkit, detailed-balance and radiative--thermodynamic analyses place TPVs on a power--efficiency landscape bounded by Carnot and exergy (Landsberg) limits and identify spectral bandwidth as a central control knob governing performance.On this foundation, the thesis explores three routes for mitigating the trade-off. First, hot-carrier TPVs introduce an internal thermodynamic degree of freedom by treating the carrier subsystem as a reservoir with its own temperature and chemical potential and by harvesting carriers through energy-selective contacts; in the ideal radiative limit, this enables a single junction to emulate multicolor performance and approach Carnot efficiency at finite power. Second, an analytical framework for a near-field TPV system based on fluctuational electrodynamics derives analytical expressions for photon tunnelling between plasmonic and semiconducting media, establishing scaling laws that show how evanescent modes can deliver super-Planckian, spectrally concentrated fluxes that support high power and high efficiency simultaneously. Third, substrate engineering is shown to be instrumental in near-field TPV design: in an ITO/InAs case study, optimizing thin, low-loss plasmonic films with tuned plasma frequency and thickness reshapes the tunnelling spectrum, concentrating useful above-bandgap transfer while suppressing sub-bandgap losses, and shifts electrical power–efficiency curves outward in the radiative limit.This thesis reframes the TPV power--efficiency compromise as malleable rather than fixed: while the global thermodynamic frontiers are immutable, the effective frontier accessible to practical architectures can be steered through controlled interventions in spectra, modes, and carrier energetics. The analytical models, thermodynamic benchmarks, and optimization strategies developed here provide a principled basis for designing and evaluating TPV systems that combine photonic control, hot-carrier extraction, and near-field coupling, and for assessing these theoretical results in relation to realistic material and device constraints.
  
• STAMMER, PHILIPP MAXIMILIAN: Photons and Information -- A modern approach to strong-field quantum optics
  
  Author: STAMMER, PHILIPP MAXIMILIAN
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN PHOTONICS
  Department: Institute of Photonic Sciences (ICFO)
  Mode: Normal
  Deposit date: 06/05/2026
  Deposit END date: 19/05/2026
  Thesis director: LEWENSTEIN, MACIEJ
  Thesis abstract: Photons and Information are notions indispensable for modern quantum technologies, and their interplay provides the foundation of understanding the quantum nature of light. In particular, photons are robust information carrier and substantiate the inherent discreteness of the electromagnetic radiation field. The information of photons, and their intrinsic quantum fluctuations, are manifested in the measurement of correlation functions of the field. Central to the understanding of photon fluctuations is the quantum theory of optical coherence, and the photon signature can reveal itself in different properties. Furthermore, the measurement of correlations is needed from an information theoretic perspective to distinguish classical from quantum signatures. These concepts ultimately lead to the original goal of the present Thesis:How can we understand the notion of photons and information in a modern strong field quantum optics perspective?Strong field quantum optics aims for the generation, characterization and the control of quantum light, beyond the conventional wisdom of strong field physics. In the traditional approach to strong field phenomena, in particular the photon up-conversion process of high-order harmonic generation, the field was merely treated classically. And hence, the notion of the photon could not exist in such descriptions. However, recent advances in the field of strong field quantum optics have revealed new signatures unaccountable by classical theory. This includes the generation of genuine non-classical states of light from strong field phenomena, revealing the intrinsic entanglement between all field modes in the process of high-order harmonic generation, or finding quantum signatures in the photon emission process itself via photon anti-bunching. In this Thesis, we reveal all such properties of the generated light field, and establish the underlying theories for the description of these phenomena. This includes the development of the quantum theory of optical coherence for high-order harmonic generation, as well as introducing new quantum information theoretic perspectives to strong field quantum optics. Therefore, the work presented in this Thesis provides the foundation for the modern formulation of quantum optical phenomena in strong field physics.
  

DOCTORAL DEGREE IN SIGNAL THEORY AND COMMUNICATIONS

• TSIAMAS, IOANNIS: Robust and Data-Efficient End-to-End Speech Translation: Segmentation, Cross-Modal Alignment, and Prosody-Aware Evaluation
  
  Author: TSIAMAS, IOANNIS
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN SIGNAL THEORY AND COMMUNICATIONS
  Department: Department of Signal Theory and Communications (TSC)
  Mode: Normal
  Deposit date: 15/05/2026
  Deposit END date: 29/05/2026
  Thesis director: RODRIGUEZ FONOLLOSA, JOSE ADRIAN | RUIZ COSTA-JUSSA, MARTA
  Thesis abstract: End-to-end (E2E) Speech Translation (ST) offers a streamlined alternative to traditional cascaded systems, promising lower latency and reduced error propagation. However, its adoption in real-world scenarios is currently hindered by three critical bottlenecks: input processing challenges (mismatch between static data and continuous audio), training data scarcity, and evaluation limitations regarding paralinguistic information.This dissertation addresses these limitations through a cohesive set of methodological and architectural innovations. First, we tackle the segmentation and input processing challenge. We introduce Supervised Hybrid Audio Segmentation (SHAS), a method that effectively bridges the gap between manual training segmentation and automatic inference segmentation, allowing E2E models to process continuous audio streams with minimal performance loss. Building on this, we propose SEGAUGMENT, a data augmentation strategy that utilizes training data re-segmentation to maximize the utility of existing datasets, significantly improving performance in low-resource settings.Second, we tackle data scarcity by developing methods for zero-shot speech translation through cross-modal alignment. We introduce ZEROSWOT, which leverages Optimal Transport to align speech encoders with massively multilingual machine translation models at the subword level, enabling translation without paired ST data. We further refine this approach with CHARSONAR, demonstrating that character-level modeling significantly improves cross-lingual and cross-modal transfer, achieving state-of-the-art results, despite being a zero-shot method.Finally, we investigate the semantic richness of E2E translations. We present a focused evaluation on prosody, revealing that while current E2E architectures possess the internal capacity to represent paralinguistic features like intonation and stress, they often fail to manifest these in the final translation.Collectively, these contributions advance the state of the art by creating E2E ST systems that are more robust to unsegmented inputs and more data-efficient, while also providing insights into the limitations of current E2E systems in preserving the nuances of spoken communication.
  

DOCTORAL DEGREE IN STRUCTURAL ANALYSIS

• GUO, ZHIMING: Study on HTPB propellant passivation, mixing, casting and curing processes by experiment and simulation
  
  Author: GUO, ZHIMING
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN STRUCTURAL ANALYSIS
  Department: Department of Civil and Environmental Engineering (DECA)
  Mode: Article-based thesis
  Deposit date: 11/05/2026
  Deposit END date: 22/05/2026
  Thesis director: ROSSI BERNECOLI, RICCARDO | FU, XIAOLONG
  Thesis abstract: This study investigates the key physical issues involved in the four propellant production steps (passivation, kneading, casting, and curing) through a combination of experimental and numerical simulations.In this experimental study using HTPB propellant ingredients as raw materials, we first investigate the passivation and dehydration (similar to reduced pressure micro-boiling) of this raw material (N-butylnitroxyethylnitramine (BuNENA)). Next, we add other materials (including liquids and granules) to the passivated raw material and mix them in a vertical kneader. The resulting propellant slurry is then cast into a specific mold. Finally, we investigated the solidification of the propellant samples formed in the mold.In this numerical simulation study, First, the passivation process (bubble generation and movement) of the BuNENA material was studied through numerical simulation. Next, the mixing process of the propellant in a vertical kneader was investigated. The uniformity and flow characteristics of the HTPB propellant material were studied under stirring conditions.Finally, the slurry casting process was simulated, and finally, the curing of the cast propellant model was simulated.This paper contains the following research contents:(1) A passivation experimental device was established and experiments were carried out using a principle similar to reduced pressure micro-boiling; in the fluid dynamics simulation model, the Lagrangian framework was applied to track the formation and movement of bubbles, and the bubbles themselves were modeled as rigid spheres subjected to buoyancy and viscous forces. The Euler framework based on variational multiscale (VMS) was used to simulate the fluid around the bubbles. The bubble movement was analyzed. By combining experimental and simulation methods, the passivation process of BuNENA was analyzed in detail, which is of substantial significance in the field of passivation of composite solid propellants.(2) A computational fluid dynamics (CFD) method was used to establish a digital simulation model of the mixing process of the vertical kneader. Changes in various flow field related characteristics of the vertical kneader were analyzed. The mixing performance of the propellant slurry in the kneader was studied by establishing a mixing uniformity index analysis method. The accuracy of the simulation results was verified by real kneading experiments, SEM-EDS and density experiments. (3) The vacuum casting process was optimized by combining experiments and numerical simulations. First, the shear thinning behavior was revealed through rheological tests, and the Herschel-Bulkley model parameters confirmed non-Newtonian fluid characteristics. The variational multi-scale finite element method was used to simulate and analyze the vacuum casting process of HTPB propellant slurry. Second, the flow rate and impact force of droplets under different vacuum pressures were studied by combining real-time image recognition with machine vision and Kalman filtering. (4) Finally, the curing reaction kinetic model of HTPB propellant was studied by the non-isothermal DSC method. The distribution and evolution of the internal temperature and temperature degree of the propellant during the molding process were analyzed using a thermochemical model. The temperature gradient and curing time variation of the propellant curing process were explored by the thermocouple-integrated method.
  
• RUBIO SERRANO, RAUL: DEVELOPMENT AND APPLICATION OF AN EIFEM-BASED SURROGATE MODEL FOR STRUCTURAL MODELLING, PRECONDITIONING AND OPTIMIZATION
  
  Author: RUBIO SERRANO, RAUL
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN STRUCTURAL ANALYSIS
  Department: Department of Civil and Environmental Engineering (DECA)
  Mode: Normal
  Deposit date: 14/05/2026
  Deposit END date: 28/05/2026
  Thesis director: HERNANDEZ ORTEGA, JOAQUIN ALBERTO | FERRER FERRE, ALEX
  Thesis abstract: The continuous demand for performance in modern engineering has led to the design of highly complex structures. Analyzing these designs via the Finite Element Method (FEM) requires solving high-dimensional systems with a prohibitive computational cost when multiple solves are needed, such as in structural optimization or non-linear problems. To mitigate this, Reduced Order Models (ROMs) seek approximations in a lower-dimensional space. Traditional global ROMs obtain fast and accurate approximations but lack topological flexibility to handle continuous geometric variations, which impairs an efficient decoupling between offline training and online execution.To overcome these limitations, this thesis builds upon the Empirical Interscale Finite Element Method (EIFEM), originally introduced for the multiscale analysis of heterogeneous structures. Formulated within the standard FEM paradigm, EIFEM replaces classical shape functions with precomputed operators learned in an offline stage, aligning with data-driven principles. Unlike traditional approaches based on nested hierarchies, EIFEM directly relates fine-scale and coarse-scale behaviors through these operators. Building on this foundation, this work investigates its application to structural beam modeling, solver acceleration, and optimization.First, the thesis addresses the modeling of complex composite structures using EIFEM, where coarse-scale degrees of freedom coincide with those of a standard beam formulation. This beam Reduced Order Model (bROM) bridges full-field 3D elasticity and reduced 1D models. Kinematic assumptions are no longer prescribed a priori, but learned from offline 3D simulations. The dimensionality reduction inherent to EIFEM yields a data-driven beam element capable of capturing complex behaviors, such as orthotropy, while maintaining full compatibility with standard FEM implementations.Second, the focus shifts to the acceleration of exact numerical solvers. EIFEM is used as a coarse space preconditioner within Conjugate Gradient (CG) iterative solvers. By exploiting localized static condensation in a reduced space, this strategy drastically decreases the condition number of large-scale systems. This enables fast, full-fidelity solutions on standard desktop architectures without relying on High-Performance Computing (HPC) environments.Finally, the thesis tackles the computational cost of structural design by extending the EIFEM framework to parametric settings. The main challenge lies in efficiently evaluating interscale operators for varying geometric configurations of the unit cell during optimization. Since directly interpolating all entries of these high-dimensional mappings would be prohibitive, the Discrete Empirical Interpolation Method (DEIM) is used to identify a reduced set of representative entries. These are interpolated with respect to geometric parameters to reconstruct the full operators. This strategy enables fast, localized evaluations without requiring global matrix reassembly, accelerating the optimization process while preserving the modularity of the framework.In conclusion, this thesis exploits the offline/online paradigm of localized reduced-order modeling using EIFEM, which provides precomputable operators that encode fine-scale behavior. This approach derives data-driven beam models from 3D elasticity, accelerates exact solvers via data-informed coarse spaces, and enables efficient optimization through parametric operator interpolation, thus aligning standard FEM practice with the machine learning paradigm.
  
• SHANG, CHENGSHUN: A Numerical Framework Based on the Discrete Element Method for Modeling Weakly and Strongly Cemented Sands
  
  Author: SHANG, CHENGSHUN
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN STRUCTURAL ANALYSIS
  Department: Department of Civil and Environmental Engineering (DECA)
  Mode: Normal
  Deposit date: 14/05/2026
  Deposit END date: 28/05/2026
  Thesis director: BARBU, LUCIA GRATIELA | CELIGUETA JORDANA, MIGUEL ANGEL
  Thesis abstract: Cemented sands are widely encountered in natural and engineered systems, ranging from biocemented soils to sandstones, where inter-particle bonding plays a critical role in mechanical behavior. Accurately modeling the influence of cementation on deformation and failure remains challenging due to the complex coupling between granular fabric and cementation characteristics at the particle scale. This thesis establishes a comprehensive numerical framework based on the Discrete Element Method (DEM) for modeling weakly and strongly cemented sands, integrating numerical developments, micromechanical modeling, and experimental validation.Following a modeling philosophy of progressing from simple to complex, cemented sand is represented as a combination of a clean sand skeleton and superimposed cementation. This approach enables a clear separation between granular and bonding contributions, improves physical interpretability, and reduces excessive numerical calibration. The research comprises theoretical developments in DEM methodology and application studies covering non-cemented, weakly cemented, and strongly cemented granular materials, systematically validated against experimental data.At the theoretical level, a standardized framework for particle packing generation is proposed, explicitly formalizing tacit knowledge embedded in empirical procedures. An Improved Radius Expansion with Servo control and Random shifting (IRESR) algorithm is introduced to ensure robust stress control and mitigate boundary effects, and the open-source tool DEMGen is developed to generate and characterize representative particle packings. Two bonded-particle models are established: an improved Parallel Bond Model (PBM) and a Parallel Bond Bilinear Damage Model (PBBDM), the latter incorporating fracture-energy-based progressive damage and post-peak softening. In addition, a machine learning (ML)-accelerated parameter calibration strategy is developed to improve calibration efficiency while maintaining accuracy.The framework is applied to non-cemented sand, weakly cemented sand, and strongly cemented sand. Biocemented sands are modeled using micro-CT-derived calcite characteristics, while Fontainebleau sandstone is simulated using a physics-based particle overgrowth approach. The results reveal a transition from contact-dominated to bond-dominated behavior with increasing cementation, demonstrating the robustness and generality of the proposed framework.
  

DOCTORAL DEGREE IN SUPPLY CHAIN AND OPERATIONS MANAGEMENT

• MAQUIRRIAIN ANTOÑANZAS, JAVIER: Algoritmos matheurísticos para la programación de mantenimientos preventivos
  
  Author: MAQUIRRIAIN ANTOÑANZAS, JAVIER
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN SUPPLY CHAIN AND OPERATIONS MANAGEMENT
  Department: Department of Management (OE)
  Mode: Normal
  Deposit date: 05/05/2026
  Deposit END date: 18/05/2026
  Thesis director: GARCÍA VILLORIA, ALBERTO | PASTOR MORENO, RAFAEL
  Thesis abstract: This doctoral thesis addresses the problem of cyclic preventive maintenance scheduling in industrial systems, which is NP-hard due to its combinatorial complexity. The problem consists of defining a maintenance policy P that schedules the maintenance of M machines over a cyclic sequence of T time periods, where at most one machine can be serviced in each period. The objective is to determine a policy that minimizes the total costs incurred. To this end, mathematical models and matheuristic algorithms are developed and evaluated.A cost structure combining linear operating costs and stepwise maintenance costs is proposed, providing a more realistic representation of behaviour observed in industrial environments. Based on this structure, two variants of the problem are defined: • Cyclic preventive maintenance scheduling with a predetermined cycle length (SECIMAP_Tpred), in which the maintenance cycle length T is fixed in advance.• Cyclic preventive maintenance scheduling with a variable cycle length (SECIMAP_Tvar), in which T is treated as a decision variable to be determined within the problem.For the SECIMAP_Tpred variant, an exact mathematical model (MMat) is formulated, together with a relaxed version (MPR) that enables the computation of lower bounds, which are subsequently used to assess the quality of the designed (mat)heuristic algorithms. Since MMat is computationally inefficient for industrial-sized instances, three matheuristics (MH1P, MH2P, and MHFI) integrating mathematical programming and heuristic strategies are developed. A sequential procedure (MHSEC) that executes the three matheuristics and retains the best solution obtained is also proposed, achieving an average deviation of at most 3.19% from the optimal solution. In addition, a matheuristic cocktail (COMH) is developed which, by applying an early termination technique based on adaptive prioritization of subalgorithms and cancellation of non-promising solutions, obtains the same results as MHSEC while reducing execution times by 53.37%.The study is then extended to the SECIMAP_Tvar variant, in which the cycle length T is optimized. For this case, a new mathematical model for the relaxed problem (MTI) is developed, together with four hybrid algorithms combining metaheuristic procedures and matheuristic methods (MTI+MHFI, ALG1P, ALG2P, and ALGFI), a sequential algorithm (ALGSEC), and an algorithm cocktail (COALG) designed to minimize computational times. The best resolution procedures for the variable-T case (ALGSEC and COALG) achieve an average deviation of at most 1.43% from the optimal solution. Furthermore, COALG attains the same solutions as ALGSEC while reducing execution times by 33.2% through the use of early termination and intelligent pruning techniques that prevent the exploration of solution configurations unable to improve upon the best solution found so far.The comparative analysis of the results obtained for both problem variants (SECIMAP_Tpred and SECIMAP_Tvar) shows that treating T as a decision variable expands the solution space and, consequently, enables the derivation of maintenance policies that achieve a 2.68% reduction in costs.Finally, the thesis presents the general conclusions, the main methodological contributions (including the introduction of stepwise cost structures or the systematic use of lower bounds computed via mathematical programming to guide heuristic search) and directions for future research.
  

DOCTORAL DEGREE IN THERMAL ENGINEERING

• BAHRAMIAN, LINDA: Numerical assessment of parcel modeling and inertial particle separator efficiency in polydisperse two-phase flows
  
  Author: BAHRAMIAN, LINDA
  Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
  Programme: DOCTORAL DEGREE IN THERMAL ENGINEERING
  Department: Department of Heat Engines (MMT)
  Mode: Normal
  Deposit date: 11/05/2026
  Deposit END date: 22/05/2026
  Thesis director: OLIET CASASAYAS, CARLES | PEREZ SEGARRA, CARLOS DAVID
  Thesis abstract: This thesis addresses fundamental and applied aspects of disperse two-phase flow modeling within the Eulerian–Lagrangian approach, with particular emphasis on the development and assessment of parcel modeling and the conservation-consistent two-way coupling approach, progressing toward application of an Inertial Particle Separator (IPS).First, the conservation properties of the two-way coupling formulation are analyzed to ensure consistent momentum exchange between the carrier and dispersed phases. The numerical implementation preserves global conservation principles and physically consistent kinetic energy evolution.The numerical method is first assessed in Direct Numerical Simulation to provide a reference solution and is subsequently extended to Large Eddy Simulation (LES) to address more realistic, application-oriented flow conditions. Building upon this foundation, a novel hybrid parcel modeling strategy is developed by combiningthe Number Fixed Model and the Volume Fixed Model for a particle distribution. The proposed approach is then validated against benchmark cases and offers an effective compromise between computational cost and predictive accuracy.Subsequently, a comprehensive numerical investigation of an IPS device is performed using LES, Improved Delayed Detached Eddy Simulation, and Reynolds-Averaged Navier–Stokes (RANS) turbulence models. The influence of turbulence resolution, flow split ratio, and Reynolds number on separation efficiency is analyzed for different particle sizes. The results indicate that turbulence modeling affects drag-dominated particles, while segregation of larger particles is mainly driven by inertial effects. This study presents a comparative assessment of turbulence modeling strategies and their impact on IPS performance prediction.The numerical framework is then extended to icing conditions. Water droplet impingement and ice accretion are first validated using a canonical cylinder benchmark. The methodology is then applied to the IPS configuration, where ice growth alters the internal geometry. Following ice accretion, solid particles are injected into the modified geometry to evaluate the separation efficiency using Lagrangian tracking, highlighting a reduction in efficiency for inertia-dominated particles as a result of scavenge blockage and modified wall interactions.In this context, a key original contribution of this study is the development and implementation of a dedicated particle–ice wall collision model. To address the lack of suitable models for solid particles impact on ice-covered surfaces in IPS, a restitution-based formulation was developed through a structured review and adaptation of existing collision models.This approach ensures a physically consistent prediction of particle rebound behavior on ice-covered surfaces.Overall, this thesis advances the predictive capability of Computational Fluid Dynamics tools for disperse two-phase flows by enhancing the implementation of conservation-consistent two-way coupling, proposing a physics-guided hybrid parcel model, quantifying the sensitivity of turbulence modeling in IPS efficiency, and introducing a novel particle–wall collision model under adverse icing conditions. The results contribute to both the methodological development of Eulerian–Lagrangian modeling and the reliable simulation of aeronautical particle separation systems.