DOCTORAL DEGREE IN APPLIED MATHEMATICS

• GONZALEZ HERNANDEZ, LAURA: On families of prime ideals with an unbounded minimal number of generators in a three-dimensional power series ring
  
  Author: GONZALEZ HERNANDEZ, LAURA
  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: 27/01/2026
  Reading date: 27/03/2026
  Reading time: 16:00
  Reading place: Sala d'Actes de l'FME, Edifici U, Campus Sud
  Thesis director: PLANAS VILANOVA, FRANCESC D'ASSIS
  Thesis abstract: This thesis deals with the existence of families of prime ideals in the power series ring k[[x,y,z]] with an unbounded minimal number of generators.We begin by studying in-depth the related results of Moh on the area. We reprove and generalize a result of Moh which gives a lower bound on the minimal number of generators of an ideal in k[[x,y,z]]. In doing so, we demonstrate that the minimal number of generators of Moh’s prime P3 might decrease depending on the characteristic of the field k. This result contradicts a previous statement made by Sally and leaves as an open problem finding families of prime ideals in k[[x,y,z]] with an unbounded minimal number of generators, when the characteristic of k is different from zero. The main result of this thesis is the construction of a new family of prime ideals in k[[x,y,z]] with an unbounded minimal number of generators, explicitly described, up to constant coefficients, which improves all the former results. The construction and analysis of these families rely on the theory of numerical semigroups and the study of binomial matrices.We first study the numerical semigroup S spanned by three consecutive natural numbers, a,a+1,a+2. We define and characterize the set of elements whose factorizations have all the same length, ULF(S), We provide an explicit description of their factorization sets and a natural partition based on the length and the denumerant. Moreover, by using Apéry sets and Betti elements, we are able to extend some of these results to any general numerical semigroup G. These findings link the structural properties of S directly to the defining ideals of the semigroup rings k[t^a,t^b,t^c], providing a bridge between factorization theory and the minimal generating sets of the corresponding prime ideals.In addition to our particular study of the numerical semigroup S, we need to work with binomial matrices. We derive closed formulae for binomial determinants and calculate bases to left nullspaces of some special binomial matrices. Additionally, we provide an alternative proof for the positivity of binomial determinants, originally shown by Gessel and Viennot. Finally, we display our new family of prime ideals with unbounded minimal number of generators in k[[x,y,z]], where k is a field of characteristic zero. These primes are obtained as the kernel of a quasi-monomial algebra homomorphism. Up to constant coefficients, we give a description of their minimal generating polynomial sets. The advantage of our family with respect to some previous work is the explicit description of the minimal generating sets and the simplicity of the exponents of the monomial presentation.
  

DOCTORAL DEGREE IN ARCHITECTURAL DESIGN

• YARASCA AYBAR, CRISTIAN PEDRO: La arquitectura rural en el Qhapaq Ñan del Cusco: paisajes culturales y arquitecturas del borde
  
  Author: YARASCA AYBAR, CRISTIAN PEDRO
  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 DESIGN
  Department: Department of Architectural Design (PA)
  Mode: Normal
  Deposit date: 24/02/2026
  Reading date: pending
  Reading time: pending
  Reading place: pending
  Thesis director: CUROS VILA, JUAN
  Thesis abstract: The Qhapaq Ñan (Andean Road System) is the largest pre-Hispanic infrastructure in the Americas, with an estimated length of over 30,000 km, and currently occupies the territory of six South American countries (Peru, Bolivia, Ecuador, Chile, Argentina, Ecuador, and Colombia). Peruvian territory is home to most (25,000 km) of the total Andean Road System. Despite being built in the 15th century during the Inca Empire, this great road network continues to be used by many Peruvian Andean communities for their daily activities of communication, herding, and cultural festivities. For this reason, the Qhapaq Ñan was declared a World Heritage Site by UNESCO in 2014, for its exceptional transnational cultural value.The Cusco region (located in the southern Peruvian Andes) contains approximately half (13,000 km) of the total length of the Qhapaq Ñan in Peru. The sections of the Inca Trail in Cusco are found in diverse geographical contexts, starting from the main city and connecting various rural settlements in heterogeneous Andean ecosystems. These pre-Hispanic roads in rural areas are part of the social, cultural, and spatial fabric of Cusco communities, forming cultural landscapes in constant motion and rural architecture. The immediate architecture of the Qhapaq Ñan consists of roadways, side walls, stone alignments, drainage systems, bridges, stairways, and tunnels, which shape and protect Andean roads. These elements, grouped under the name of edge architecture, pass through Andean communities and are associated with their rural architecture, consisting of dwellings, agricultural plots, corrals, and archaeological sites. The edge and rural architecture associated with the Qhapaq Ñan is integrated into an Andean cultural landscape with symbolic, ritual, and natural value. However, modern dynamics such as tourist attractions, rural depopulation, and contemporary construction are part of this confrontation between historical heritage and current demands. Therefore, this thesis addresses the analysis of the architecture and cultural landscapes of the Peruvian Andes, focusing on the interactions between human communities, the built environment, the natural environment, and the Qhapaq Ñan.The thesis takes the “Cusco-La Raya” section as a case study because it is the longest and most culturally significant section in the Cusco region. Within this section, three sections declared World Heritage Sites are specifically studied: “Rumiqolqa-Chuspitakana,” “Santa Cruz de Occobamba,” and “Raqchi-Qquea.” Direct observation and recording were used through pedestrian tours of the sections studied, documentary reviews, geographic information systems, and interviews. Rural architecture associated with the Qhapaq Ñan is presented as a reflection of sociocultural and historical processes, highlighting its relevance for territorial sustainability in the contemporary context. This thesis presents the results through a series of diagrams, maps, aerial photographs, graphic dissections, and systematized architectural notes. Cultural landscapes are multifunctional spaces that combine agricultural, residential, and symbolic activities. Their preservation is essential for maintaining the balance between biodiversity and sustainable practices in rural communities. However, significant threats have been identified, such as uncontrolled urbanization, the loss of traditional knowledge, and the introduction of modern materials that alter the authenticity of rural structures. The study concludes that the preservation and revaluation of Andean architecture and cultural landscapes associated with the Qhapaq Ñan are essential to promote sustainability and social cohesion in the face of current challenges in the Peruvian Andes.
  

DOCTORAL DEGREE IN BIOINFORMATICS

• MANZINI, ENRICO: Deep Learning Strategies for Longitudinal EHRs: Patient Trajectory Modelling, Survival Analysis and Semantic Representations in Chronic Diseases
  
  Author: MANZINI, ENRICO
  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 BIOINFORMATICS
  Department: School of Mathematics and Statistics (FME)
  Mode: Article-based thesis
  Deposit date: 02/02/2026
  Reading date: 25/03/2026
  Reading time: 12:00
  Reading place: Sala de Juntes FMECampus SudEdifici U
  Thesis director: PERERA LLUNA, ALEXANDRE
  Thesis abstract: The growing availability of longitudinal electronic health records (EHRs) offers unprecedented opportunities to advance personalized medicine and improve patient care. Motivated by recent breakthroughs in deep learning (DL) and the transformative impact of large language models and foundation models in other domains, this thesis explores how similar computational approaches can be adapted to the complexities of healthcare data. The work investigates how neural architectures can capture temporal patterns, non-linear interactions, and latent structures within sparse, heterogeneous clinical datasets, enabling the prediction of disease trajectories and clinical outcomes with greater precision. A central focus is on chronic diseases, particularly type 2 diabetes and chronic obstructive pulmonary disease, where patient trajectories vary widely and outcomes depend on dynamic interactions among biomarkers, treatments, and comorbidities. By developing and adapting attention-based and recurrent neural networks, the thesis shows that DL can predict disease evolution, identify patient subgroups with distinct clinical trajectories, and estimate the risk of key clinical events over time. Hence, by learning meaningful representations of patient histories, these models support dynamic, data-driven decision-making that moves beyond static risk scores toward truly personalized care. In doing so, the work shows how the principles underlying generative foundation models and LLMs—such as sequence modelling, attention mechanisms, and contextual embeddings—can be translated into clinical applications, enabling DL models to generate interpretable, actionable insights from complex longitudinal data. Beyond predictive performance, this thesis emphasizes interpretability, usability, and the integration of patient-generated data. Techniques to translate patient-reported symptoms into structured medical ontologies, for example, illustrate how artificial intelligence can bridge communication gaps, enhance data quality, and facilitate real-world implementation. The work also reflects on methodological considerations, highlighting the importance of tailoring architectures to irregular, multidimensional clinical data, leveraging pre-training and defining embedding strategies to improve robustness, generalisation, and reproducibility. Specifically, this thesis is organized around four complementary lines of research, each addressing a different aspect of applying deep learning to healthcare data. The first focuses on unsupervised techniques, clustering longitudinal trajectories in type 2 diabetes and identifying patient subgroups with distinct disease progression patterns and clinical profiles to support personalized management. The second and third lines focus on supervised learning: the second develops a transformer-based model to predict longitudinal outcomes, emphasizing model architecture and the adaptation of attention-based networks to irregular, multidimensional EHR data, while the third is centred on modelling the complex dynamics of clinically relevant outcomes through deep survival analysis. Finally, the fourth line investigates the translation of layperson medical terminology into structured ontologies, enabling patient-reported data to be integrated into standardized clinical vocabularies and facilitating usability and research applications. Taken together, the four lines of research of this thesis demonstrate both methodological innovations and clinically meaningful results, spanning predictive modelling, patient stratification, and the enhancement of healthcare data usability. They represent a step forward in precision medicine and AI-assisted healthcare, showing that thoughtfully designed deep learning frameworks can transform complex, heterogeneous EHR data into interpretable, actionable knowledge.
  

DOCTORAL DEGREE IN BIOMEDICAL ENGINEERING

• FUENTES LLANOS, JUDITH: Development of 3D Bioengineered Skeletal Muscle-based Bioactuators for Biorobotic and Biomedical Applications
  
  Author: FUENTES LLANOS, JUDITH
  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 BIOMEDICAL ENGINEERING
  Department: Department of Materials Science and Engineering (CEM)
  Mode: Article-based thesis
  Deposit date: 11/02/2026
  Reading date: 10/04/2026
  Reading time: 11:00
  Reading place: Sala d'Actes, Edifici Vèrtex, Campus Diagonal Nord, Vèrtex (VX), Plaça d'Eusebi Güell, 6, 08034 Barcelona
  Thesis director: SÁNCHEZ ORDÓÑEZ, SAMUEL | GUIX NOGUERA, MARIA
  Thesis abstract: Biohybrid systems are engineered constructs that integrate living materials, like cells or tissues, with synthetic components, such as electronics, mechanical structures, or other artificial materials. This integration aims to leverage the unique capabilities of biological materials, including self-assembly, responsiveness to certain stimuli, adaptability, and self-repair, being features challenging to replicate in their synthetic counterparts. By combining these traits with the robustness and compliance of synthetic structures, biohybrid systems emerge as versatile platforms capable to both adapt and actuate in dynamic environments. Among the various biological components explored, skeletal muscle stands out due to its high force-to-weight ratio, controllable contraction, and compatibility with 3D biofabrication strategies, making it ideal for applications in soft robotics, drug testing, and regenerative medicine.This PhD thesis focuses on the development of 3D bioengineered skeletal muscle bioactuators, addressing key aspects from the biofabrication to functionality improvement, actuation control, and regenerative capacity.We developed and optimized a Pluronic-Assisted Coaxial 3D bioprinting (PACA-3D) method capable of biofabricating fascicle-like skeletal muscle bioactuators with improved maturation, sarcomere formation and contractile force when compared to muscles generated using conventional extrusion-based 3D bioprinting. To improve actuation and directional control, we introduced magnetic responsiveness into the bioactuators by developing a biocompatible ferrofluid-based bioink, used to fabricate the Ferromuscle, in collaboration with Waterloo and Aalto Universities. Such bioactuators exhibited improved cell alignment and force output, as well as magnetically guided actuation. In collaboration with the University of Cagliari (UniCa), we also evaluated the integration of flexible Organic Field-Effect Transistor (OFET)-based strain sensors for real-time monitoring of contractile behavior. These devices allowed tunable force sensing, showing no signal cross-talk when using standard electrical stimulation protocols, which is paving the way for closed-loop stimulation strategies and automated drug screening platforms. Moreover, we evaluated the self-healing capacity of the bioactuators after damage conditions, designed to mimic the mechanical stress that they typically undergo during manipulation of biohybrid systems. We observed partial to full force recovery and structural remodeling without external interventions, although the underlying biological mechanisms remain to be elucidated.Finally, additional collaborative work that enriched this thesis will be also discussed. Such studies include the integration of tendon-like structures to develop muscle-tendon unit (MTU) actuators with enhanced biomimicry, performance, and stability (ETH Zurich). Moreover, it will also be covered the design of biohybrid flexure mechanisms powered by skeletal muscle, evaluating how skeleton architecture and voltage- or current-based stimulation influence actuation output (SSA and UniCa).Altogether, the work presented in this thesis contributes to the field of biohybrid robotics by combining advanced fabrication, actuation control, integrated sensing, and regenerative capacity. These developments move us closer to robust, autonomous, and functional biohybrid machines for applications in soft robotics, tissue engineering, and biomedical research.
  

DOCTORAL DEGREE IN CIVIL ENGINEERING

• JEBBAD, RAGHDA: Impacts of climate change on the ports of the Southwestern coast of the Alboran Sea
  
  Author: JEBBAD, RAGHDA
  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: 18/02/2026
  Reading date: pending
  Reading time: pending
  Reading place: pending
  Thesis director: MÖSSO ARANDA, OCTAVIO CESAR | SIERRA PEDRICO, JUAN PABLO
  Thesis abstract: Climate change is altering the coastal conditions that ports are designed to withstand, through sea-level rise and changes in storm-driven wave regimes. These changes can reduce operability, increase overtopping impacts on exposed structures, and affect harbour agitation and breakwater performance. This thesis develops an applied and integrated framework to translate offshore climate projections into port‑scale impact indicators and adaptation pathway planning for Moroccan Mediterranean ports along the southwestern Alboran Sea.Offshore wave conditions are characterised using a four‑dataset Med-CORDEX ensemble under RCP4.5 and RCP8.5, and propagated to the nearshore with a regional SWAN model featuring port‑scale nesting. Combined with sea level rise projections, these forcings are used to quantify four port‑impact indicators: berthing inoperability based on still‑water‑level exceedance relative to minimum freeboard requirements; wave overtopping discharge at exposed structures; harbour agitation exceedance within the basin; and breakwater stability under extreme forcing. The overtopping assessment is applied across eight ports to establish relative exposure and identify the most exposed sites. Berthing inoperability and an indicative dock‑raising cost estimate are developed for Tangier‑Med. A multi‑process assessment is carried out for Al Hoceima, together with an applied adaptation‑pathways case study that links impact levels to response options over time. The thesis also develops an uncertainty‑evaluation framework to support the interpretation of wave‑driven port impact projections.
  

DOCTORAL DEGREE IN COMPUTATIONAL AND APPLIED PHYSICS

• BLANCO CASARES, ANTONIO: A Numerical Framework for Solving Complex Flow Regimes with Continuous Galerkin
  
  Author: BLANCO CASARES, ANTONIO
  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: 18/02/2026
  Reading date: pending
  Reading time: pending
  Reading place: pending
  Thesis director: LEHMKUHL BARBA, ORIOL | MIRA MARTÍNEZ, DANIEL
  Thesis abstract: This thesis investigates a stable and high-order numerical formulation for solving a variety of flow problems using the continuous Galerkin method, including reacting, compressible, and incompressible flows. Reliable and accurate numerical tools for such problems are crucial in many real-world applications, ranging from aerodynamics and industrial processes to medical studies, where high-fidelity and computational efficiency are simultaneously required. Achieving stable solutions in advection-dominated problems, particularly on nonuniform meshes, remains a fundamental challenge.To address this, the thesis develops a stabilization strategy well-suited for high-order elements, such as the spectral ones, designed to effectively eliminate numerical instabilities while introducing minimal numerical dissipation to achieve a high accuracy solution. Based on recent literature, we have focused on stabilization methods based on gradient projection, which allows to detect the numerical fluctuations to effectively stabilize the system. The numerical stencil features a combination of a high- and low-order stabilization ruled by a smoothness sensor. High-order stabilization is applied in smooth regions to maintain physical profiles, while low-order stabilization is activated only near strong gradients to suppress non-physical oscillations. This methodology is formulated for a generic conservation law and tested across linear advection, scalar transport in reacting flows, compressible, and incompressible Navier–Stokes problems.The numerical results show that, for turbulent premixed combustion under the low-Mach assumption, the approach handles tabulated chemistry with the flamelet model, delivering a proper representation of the flame-front. For compressible flows, a Mach-number-based smoothness sensor has been developed, effectively handling complex flow features such as shock waves. For incompressible flows, the algorithm employs a fractional step method with well defined boundary conditions, maintaining the same stabilization principles inherent to this method. In all cases, the stabilization introduces diminishing numerical dissipation with increasing polynomial order, achieving optimal convergence under both mesh refinement and polynomial order increase.These developments contribute to practical, large-scale numerical simulations that balance computational efficiency with high-fidelity results, enabling accurate prediction of real-world physical phenomena. The proposed framework provides a robust and scalable tool for computational fluid dynamics applications, with potential impact across scientific research, and industrial modeling.
  

DOCTORAL DEGREE IN COMPUTER ARCHITECTURE

• DOBLAS FONT, MAX: High-Performance Sequence Alignment: Co-Designing Algorithms and Hardware Architectures for Efficient and Scalable Acceleration
  
  Author: DOBLAS FONT, MAX
  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: 20/02/2026
  Reading date: pending
  Reading time: pending
  Reading place: pending
  Thesis director: MORETÓ PLANAS, MIQUEL | MARCO SOLA, SANTIAGO
  Thesis abstract: Over the past decade, the exponential growth of genomic data has driven significant advancements in genomics and healthcare, enabling breakthroughs in large-scale genomic studies, personalized medicine, and epidemiological surveillance. However, this rapid data expansion has also posed substantial computational challenges, particularly in sequence alignment, a cornerstone of genomic analysis. Sequence alignment is critical for applications such as disease diagnostics, population-wide genetic research, and outbreak tracking, yet it demands scalable and efficient solutions to handle the increasing data volumes and diverse use cases.This thesis addresses the efficiency-flexibility gap in sequence alignment through three key contributions. First, it introduces QuickEd, a novel sequence alignment algorithm that reduces computational complexity by combining heuristic bounding with optimal alignment. QuickEd achieves O(nŝ) complexity, where n is the sequence length and ŝ is an estimated upper bound of the alignment score, significantly improving upon the O(n²) complexity of traditional dynamic programming algorithms. By efficiently bounding the maximum alignment score, QuickEd reduces the computational burden while maintaining accuracy, making it well-suited for long-read sequencing and other demanding applications.Second, this thesis proposes GMX, a set of instruction set architecture (ISA) extensions designed to enhance the efficiency of dynamic programming-based alignment algorithms. GMX provides fundamental building-block operations for fast, tile-wise computations of the DP matrix, reducing memory footprint and computational overhead. These extensions enable seamless integration into widely used algorithms and tools, offering a cost-effective alternative to domain-specific accelerators (DSAs) while achieving comparable performance improvements.Third, the thesis presents SMX, a heterogeneous architecture that balances flexibility and performance to address the diverse requirements of real-world sequence alignment tasks. SMX integrates an ISA extension (SMX-1D) for irregular and sequential tasks and a specialized coprocessor (SMX-2D) for regular and parallel tasks, all orchestrated by a general-purpose core. This architecture supports various configurations for different sequence types (DNA, protein, and ASCII text) and alignment models, including weighted gaps and substitution matrices. By combining high performance with adaptability, SMX enables efficient acceleration of a wide range of sequence alignment applications.Together, these contributions advance the state of the art in sequence alignment, providing scalable, flexible, and efficient solutions to meet the demands of modern genomic analysis. The innovations presented in this thesis pave the way for faster and more reliable genomic analyses, facilitating critical applications such as personalized medicine, population-scale sequencing, and epidemiological surveillance in the era of long-read sequencing technologies.
  

DOCTORAL DEGREE IN ELECTRICAL ENGINEERING

• FORERO QUINTERO, JOSE FERNANDO: Flexibility Management System with Distributed Energy Resources
  
  Author: FORERO QUINTERO, JOSE FERNANDO
  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 ELECTRICAL ENGINEERING
  Department: Department of Electrical Engineering (DEE)
  Mode: Article-based thesis
  Deposit date: 19/02/2026
  Reading date: pending
  Reading time: pending
  Reading place: pending
  Thesis director: VILLAFÁFILA ROBLES, ROBERTO | MONTESINOS MIRACLE, DANIEL
  Thesis abstract: The challenge of climate change is ever demanding more technological and political efforts to respond adequately to its adverse effects, which could put life on the planet at risk. Diverse plans have been proposed to incorporate more renewable generation and digitalize the power system in a decarbonizing energy transition framework. However, such plans are either not being accomplished or are insufficient according to the Paris Agreement. At the same time, emerging technologies such as artificial intelligence, high-performance sensing and automation, and real-time information and communication systems, among others, are transforming electrical networks into smart grids, aggregating more complexity.This thesis, based on a papers compendium, first surveys the existing energy management systems with distributed energy resources, delving into their configuration, energy and flexibility sources, information and communication systems, profitability analysis and indicators, and flexibility services. After reviewing the state of the art, this thesis focuses on boosting management systems towards the real-time horizon, proposing a flexibility management system. This system is a hierarchical three-level management system based on an adaptive autoregression algorithm, cost-benefits analysis, and redispatching and unit recommitment submodules to control and manage the power, and short-term flexibility requests.To validate the proposed system, not only several simulations and sensitivity analyses are carried out, but real-world energy assets are addressed in PVZEN microgrid lab in Torino, Italy. The results demonstrate that proposed system can reduce the cost overruns of real-time power deviations of the distributed resources, leverage the excess of renewable energy, and generate profits providing flexibility services to the grid. Chapters 2, 3, and 4 show the papers related to this thesis, starting with reviewing the art status, passing through the simulations and analysis, and finishing with the essays on real-world energy assets. Main conclusions and future work are depicted at the end of the document.
  
• NOLASCO BENITEZ, EDITH: Evaluación de programas de electrificación rural desde un enfoque de sostenibilidad: Análisis del impacto del BID-FERUM II en Ecuador
  
  Author: NOLASCO BENITEZ, EDITH
  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 ELECTRICAL ENGINEERING
  Department: Department of Electrical Engineering (DEE)
  Mode: Normal
  Deposit date: 20/01/2026
  Reading date: 20/03/2026
  Reading time: 10:00
  Reading place: Aula 28.8 Sala de Conferències de l'ETSEIB
  Thesis director: GOMIS BELLMUNT, ORIOL
  Thesis abstract: Access to energy is a key driver of economic and social development, as it fosters income generation, contributes to poverty eradication, and enhances quality of life. Its significance has been acknowledged through initiatives such as the 2030 Agenda, which prioritises universal access to clean and modern energy. Nevertheless, despite notable progress, millions of people worldwide remain without access to electricity services, predominantly in rural areas of developing countries. Africa is among the most affected regions, while others, such as Latin America, the Caribbean, and East Asia, have made substantial progresses towards universal access. Factors such as the pandemic, inflation, and the global energy crisis have slowed this progress, impacting equity and sustainability in energy access.In response to this scenario, rural electrification has been approached through various technical strategies, including grid extension, stand-alone systems, and mini-grids. However, significant challenges persist regarding the sustainability and effectiveness of implemented programmes. The need for comprehensive evaluations has led to the development of methodological frameworks focused on energy sustainability.This doctoral thesis aims to evaluate a rural electrification programme using a methodology based on a sustainability framework comprising five dimensions: technical, economic, social, environmental and institutional. The evaluation is conducted at the local level, using a case study the Rural and Urban-Marginal Electrification Programme FERUM II, implemented in the province of Esmeraldas, Ecuador. Through this territorial analysis, key aspects for evaluating such programmes are identified, and specific indicators are defined to assess the achievement of objectives within each dimension. The proposed methodology seeks to offer a practical, contextualised, and replicable guide for developing countries interested in conducting rigorous evaluations of rural electrification initiatives.The research is structured in several phases: first, a systematic literature review to contextualise the problem and build the theoretical framework; then, the development of the evaluation methodology, applied to the Ecuadorian case through semi-structured interviews, questionnaires and fieldwork in rural communities. Based on the analysis of locally collected data, two fundamental questions are addressed: What elements should be considered when evaluating rural electrification programmes? and What outcomes emerge from applying the sustainability framework to the FERUM II case?Preliminary results indicate that the programme has led to substantial improvements in access to electricity services and in the quality of life of the beneficiary population in Esmeraldas. This demonstrates that the proposed sustainability framework effectively captures the multidimensional impacts of rural electrification in vulnerable contexts. The economic dimension reveals certain weaknesses, particularly regarding the promotion of productive activities and access to credit. In contrast, the social dimension shows a positive impact, with progress in education, communications, and security. From an environmental perspective, a reduction in the use of fossil fuels is observed due to the incorporation of grid electricity. Regarding the institutional component, although performance is generally acceptable, deficiencies are identified in the relationship between distribution companies and beneficiary communities, particularly in terms of user support and communication.Keywords: Rural electrification, energy sustainability, programme evaluation, energy access, social and economic development, sustainable development, Esmeraldas, Ecuador.
  

DOCTORAL DEGREE IN ELECTRONIC ENGINEERING

• ROSERO POZO, CARLOS ALBERTO: CONTRIBUTION TO THE DESIGN OF INSTRUMENTATION FOR THE SURFACE AND SUBSURFACE OF MARS
  
  Author: ROSERO POZO, CARLOS ALBERTO
  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: 22/01/2026
  Reading date: 19/03/2026
  Reading time: 11:00
  Reading place: Aula de Teleensenyament, edifici B3
  Thesis director: DOMINGUEZ PUMAR, MANUEL MARIA | JIMENEZ SERRES, VICENTE
  Thesis abstract: Accurate in situ characterization of the atmospheric environment and its interaction with planetary surfaces, as well as the thermophysical properties of those surfaces, is essential for understanding surface–atmosphere processes, supporting the development of climate predictive models, and enabling the design of exploration systems for robotic and human missions. On Mars, these measurements are strongly constrained by low atmospheric pressure, large temperature gradients, while current missions have strict limitations associated to mass, power and autonomy. This thesis addresses these challenges through the analysis, modeling, and validation of instrumentation and methodologies for planetary surface exploration.The main contribution of this work is the adaptation and validation of a wind sensor structure as a spherical thermoprobe for the characterization of regolith thermophysical properties based on the thermal impedance concept. By injecting a controlled periodic thermal excitation and analyzing the temperature response in the frequency domain, the proposed approach enables the extraction of thermal conductivity and thermal diffusivity. A detailed and realistic analytical thermal model is developed, including probe-specific parameters to improve sensor performance, together with the implementation of compensation techniques for structural heat losses, allowing the capture of the most relevant thermal impedance spectra over an extended frequency range. The methodology is experimentally validated through measurements using hollow glass microbeads and the MMS-2 simulant, the latter being representative of realistic Martian conditions, and showing good agreement with reference instruments.In addition, this thesis investigates the thermal behavior of the Martian wind sensor of the MEDA instrument, with emphasis on the supporting structures that interact with the surrounding atmosphere. The one-dimensional analytical thermal model used to describe the sensor response is evaluated through three-dimensional finite element simulations under low-density atmospheric conditions. The applicability of different convective heat transfer correlations is analyzed, showing that rarefaction effects play a key role in slender structures such as bond-wires. The results confirm the validity of the simplified analytical formulation and provide improved insight into heat transfer mechanisms in Martian wind sensing probes.Overall, this work contributes to improving the reliability of in situ measurements and to the development of compact solutions suitable for integration on landers, rovers, and small robotic platforms for future Mars surface missions or other celestial bodies.
  

DOCTORAL DEGREE IN ENVIRONMENTAL ENGINEERING

• SOUSSE VILLA, RUBEN: Dust in the atmosphere: Integrated modeling of heterogeneous chemistry, mineralogy, and optical properties
  
  Author: SOUSSE VILLA, RUBEN
  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: 16/02/2026
  Reading date: 29/04/2026
  Reading time: 12:00
  Reading place: Place: ETSECCPBUPC, Campus NordBuilding C1. Classroom: 002C/Jordi Girona, 1-308034 Barcelona
  Thesis director: PEREZ GARCIA-PANDO, CARLOS | JORBA CASELLAS, ORIOL
  Thesis abstract: Airborne mineral dust is one of the dominant aerosol components in the atmosphere and plays a central role in the Earth system through its interactions with atmospheric composition and radiation. Despite its importance, large uncertainties remain in its representation in atmospheric and climate models, particularly concerning dust heterogeneous chemistry. These reactions between dust particles and gas species play a dominant role over dust affected regions in the formation of secondary inorganic aerosols such as particulate nitrate, ammonium, and sulfate. Yet, they are still subject to strong simplifications and parameterizations in atmospheric models, which still show significant discrepancies in their representation.This Thesis aims to advance the understanding of dust atmospheric chemical processes, their dependence on mineralogical composition, and their implications for aerosol optical properties. To this end, multiple dust heterogeneous chemistry mechanisms and mineralogical representations are implemented and systematically evaluated within the MONARCH atmospheric chemistry model. Their impacts on atmospheric composition, aerosol acidity, and optical properties are assessed and evaluated against observations. Notably, this work represents the first integration into an atmospheric chemistry model of observationally constrained maps of surface mineral composition of arid surfaces derived from the recently launched NASA’s Earth Surface Mineral Dust Source Investigation (EMIT) spectrometer.The evaluation of different dust heterogeneous chemistry mechanisms highlights the importance of representing reversible secondary inorganic aerosol formation in both fine and coarse particle modes, together with an accurate treatment of dust and sea-salt alkalinity. This combined approach is shown to be necessary to achieve consistent nitrogen budgets and aerosol acidity, and to better reproduce observed atmospheric nitrate concentrations.The comparison of different mineral atlases focuses on calcite as the primary contributor to dust alkalinity, demonstrating that EMIT provides the most consistent representation of the atmospheric calcite cycle. The use of source-resolved mineralogy compared to the common assumption of globally averaged dust mineral content, reveals strong geographical contrasts in calcite abundance, particularly between the Middle East and other major dust regions such as the Sahara and East Asia. These differences in calcite lead to marked regional and size-dependent implications for aerosol acidity and secondary inorganic aerosol formation. Impacts on particulate nitrate are found to be most pronounced during short-lived, intense formation events in regions affected by both dust and anthropogenic pollution, while global burdens and long-term trends are less sensitive.The implications of dust chemistry and mineralogical representation for aerosol optical depth and single scattering albedo are generally modest, with fine-mode aerosols exerting a larger influence than coarse particles, though both remain secondary compared to strongly absorbing minerals such as iron oxides. Limitations related to the representation of absorbing carbonaceous aerosols and the absence of cloud–aerosol interactions constrain the assessment of radiative effects.Overall, this Thesis demonstrates that combining reversible dust heterogeneous chemistry with source-resolved mineralogical information from EMIT substantially improves the representation of dust–gas interactions and their impacts on atmospheric composition and aerosol optics. Remaining uncertainties highlight the need for continued development of mineralogical datasets and chemical parameterizations, as well as the inclusion of anthropogenic dust sources and additional reactive gas species. Further progress would also benefit from high-resolution modeling of small-scale dust events and from expanded observational coverage over dust-affected urban regions.
  

DOCTORAL DEGREE IN GEOTECHNICAL ENGINEERING

• ALONSO, MATÍAS: Hydro-mechanical modelling of a sealing concept for a deep geological radioactive waste repository
  
  Author: ALONSO, MATÍAS
  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: 15/12/2025
  Reading date: 16/03/2026
  Reading time: 11:00
  Reading place: ETSECCPB.UPC, Campus NordBuilding C1. Classroom: 002C/Jordi Girona, 1-308034 Barcelona
  Thesis director: VAUNAT, JEAN | OLIVELLA PASTALLE, SEBASTIAN
  Thesis abstract: Deep Geological Disposal (DGD) has emerged as the most viable solution for the final disposal of radioactive waste, offering the potential for the permanent containment and isolation of waste from the biosphere over extended timescales. Several countries have made significant progress in developing Deep Geological Repository (DGR) concepts for the permanent disposal of such waste. The long-term safety of these facilities relies primarily on the host rock—the natural barrier that plays the central role—supplemented by engineered components collectively referred to as the engineered barrier system (EBS). The EBS includes containers, backfills, buffers, and other structures designed to ensure favourable conditions for the long-term isolation of radioactive waste. The design, performance, and safety assessment of a DGR—and particularly of its EBS components—are therefore essential for the sustainable development of nuclear energy, making their study a key research area within geotechnical engineering.In this context, the main objective of this research is to contribute to the understanding and assessment of the long-term performance of a large-diameter sealing concept developed within the framework of the Cigéo project, led by the French National Radioactive Waste Management Agency (Andra). To achieve this objective, a multi-scale and multi-step numerical modelling strategy has been adopted. The approach combines detailed material characterisation with advanced constitutive modelling of the expansive core, backfill materials, and host rock, accounting for features such as inherent anisotropy and double structure. The modelling framework incorporates coupled hydro-mechanical processes, enabling the analysis of key phenomena such as the natural hydration of the sealing core, the development of swelling pressure, the resaturation and recompression of the excavation-damaged zone (EDZ), the global equilibrium of the sealing system, and the potential deconfinement of the sealing core and its associated loss of swelling capacity. The simulations address the complexity of the problem by integrating large-scale three-dimensional geometries, advanced constitutive formulations, and critical geometric details at the decimetre scale. These challenging simulations provide valuable insights into the performance and long-term integrity of the sealing structures, establishing a robust framework and a powerful tool to enhance the understanding of the behaviour of these EBS, contributing to the optimisation of repository design and safety.
  
• ESCANELLAS TUR, ANDREU: Time-dependence of rock salt fracture mechanics: Experimental an Numerical study
  
  Author: ESCANELLAS TUR, ANDREU
  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: 26/01/2026
  Reading date: 19/03/2026
  Reading time: 11:30
  Reading place: ETSECCPB.UPC, Campus NordBuilding C1. Classroom: 002C/Jordi Girona, 1-308034 Barcelona
  Thesis director: CAROL VILARASAU, IGNACIO | LIAUDAT, JOAQUÍN
  Thesis abstract: This thesis presents an experimental and numerical study on the time-dependent behaviour of fracture propagation in rock salt. The research aims to enhance the understanding of rock salt fracture mechanics, which is crucial for applications such as underground storage of hazardous waste and energy storage, whose long-term stability remains difficult to predict due to the combined effects of creep and fracture. Despite its practical significance, studies addressing the combined effects of creep and Fracture Mechanics of rock salt are scarce in the literature. To address this gap, an experimental campaign was conducted on halite specimens extracted from the Cardona Formation in Spain. Wedge Splitting Tests (WST) were performed at four loading rates spanning three orders of magnitude, complemented by uniaxial creep tests at stress levels between 2.5 and 10 MPa. The WST results reveal a clear rate dependency: peak splitting forces increase with loading rate, while the mechanical work required for fracture decreases, indicating significant contributions of creep to the fracture process at slower rates. The creep tests show non-linear viscous deformation, with approximately linear behaviour only at low stress levels. Subsequently, numerical simulations were performed using the in-house developed finite element code, which combines interface elements with an elasto-plastic constitutive law and visco-elastic continuum elements. These initial simulations partially reproduced the observed experimental trends, confirming that both bulk creep and fracture-process-zone mechanisms contribute to the observed rate dependency, but failing to match quantitatively the experimental results. To improve the modelling capabilities, two new constitutive laws for interface elements are proposed, incorporating elastic degradation, frictional behaviour, viscoelasticity and viscous dissipation processes. These formulations enhance the representation of time-dependent fracture by enabling consistent modelling of salt fracture across a wide range of loading rates.Simulations performed with the new proposed visco-elastic-damage model show improved agreement with experimental results, quantitatively reproducing the observed trends in apparent fracture energy and peak force with regard of loading rate.
  
• MORALES FLAUZÍN, GERARDO ABEL: Advances in geotechnical experimental techniques for unsaturated and liquefactable soils and tailings under different gravity accelerations (1g-Ng)
  
  Author: MORALES FLAUZÍN, GERARDO ABEL
  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: 16/02/2026
  Reading date: 27/03/2026
  Reading time: 15:00
  Reading place: ETSECCPB.UPC, Campus NordBuilding C1. Classroom: 002C/Jordi Girona, 1-308034 Barcelona
  Thesis director: PINYOL PUIGMARTI, NURIA MERCE | OLIVELLA PASTALLE, SEBASTIAN
  Thesis abstract: Flow liquefaction in contractive granular materials represents one of the most complex and hazardous failure mechanisms in geotechnical engineering, particularly in tailings dams. It is characterised by a sudden loss of shear strength under undrained conditions and by the rapid mobilisation of large volumes of material. Despite its practical relevance and the many documented failures, a detailed understanding of the internal processes governing the transition from sliding to flow remains limited, mainly due to the difficulty of obtaining experimental evidence during large deformations and post-failure stages.This doctoral thesis addresses the experimental study of post-failure behaviour, including strain localisation, retrogressive failure and flow liquefaction, through the development and application of non-invasive image-based measurement techniques. The main objective is to improve the observation, quantification and interpretation of deformation mechanisms and hydro-mechanical processes under large deformation conditions, and to apply these tools to the study of tailings failures.From a methodological perspective, the thesis refines the Eulerian–Lagrangian PIV-NP method. Its main limitations are systematically analysed, with particular emphasis on border-related errors, and specific correction strategies are proposed to enhance robustness and accuracy. These developments are validated using synthetic cases involving rigid motion and controlled deformation. In addition, PIV-NP is integrated with short-wave infrared (SWIR) imaging to allow non-intrusive measurement of surface moisture and degree of saturation in moving soils. This combined methodology, referred to as PIV-NP-Sr, is based on homographic transformations ensuring accurate spatial correspondence between visual and infrared images.The proposed techniques are first applied to a 1g test involving wetting-induced failure of a sand dam. Subsequently, a comprehensive experimental programme based on geotechnical centrifuge modelling is presented, conducted within the Geolab–SLIDAM project. This programme includes the characterisation of a mine tailing and seven small-scale centrifuge models tested under different acceleration levels, saturation conditions and failure activation mechanisms. Conventional instrumentation was deliberately minimised in favour of non-invasive imaging, allowing the full evolution of failure to be captured without disturbing material behaviour.The results enable detailed interpretation of shear band development, volumetric strain evolution and the progressive transition from sliding to flow-type behaviour. A novel observation is the occurrence of surface eruptions during post-failure stages, resembling miniature volcanoes. These events are shown to be preferentially located in zones of concentrated extensional strain. Based on the tracking of individual gas bubbles and their correlation with deformation fields, a preliminary interpretation involving gas entrapment, migration and pressurisation within the saturated granular medium is proposed.
  

DOCTORAL DEGREE IN NETWORK ENGINEERING

• KUMAR, SUNEEL: Leveraging Multi-Link Diversity for Reliable, Low-Latency Industrial Wi-Fi
  
  Author: KUMAR, SUNEEL
  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: 20/02/2026
  Reading date: 25/03/2026
  Reading time: 11:00
  Reading place: sala C4-001 de l'EETAC, campus Baix LlobregatPer videoconferència a l'enllaç: https://meet.google.com/vcm-onwi-xpc
  Thesis director: GARCIA VILLEGAS, EDUARDO | CAMPS MUR, DANIEL
  Thesis abstract: Industrial Internet of Things (IIoT) applications, such as motion control, robotics, and functional safety, are increasingly deployed over Wi-Fi to gain flexibility, mobility, and improved operational adaptability compared to wired alternatives. However, current Wi-Fi deployments struggle to deliver the strict latency, reliability, and availability guarantees demanded by these safety and time-critical workloads. This thesis addresses how Wi-Fi can be made SLA-aware, i.e., capable of meeting per-flow Service Level Agreements (SLAs) expressed in terms of delay bounds for heterogeneous industrial traffic. To this end, it investigates the combination of Time-Sensitive Networking (TSN) features, specifically Frame Replication and Elimination for Reliability (FRER) and adaptive redundancy, with emerging Wi-Fi 7/8 features such as Multi-Link Operation (MLO) and Multi-AP Coordination (MAP-Co), aiming to provide predictable and efficient wireless connectivity in demanding industrial environments.First, the thesis focuses on SLA-driven scheduling for Wi-Fi 7 MLO. An SLA-MLO scheduler is proposed that adjusts per-flow link-selection probabilities based on measured per-link delay and SLA deviation, targeting per-flow SLA compliance rather than only minimizing average delay. This is extended to a cooperative variant, C-SLA-MLO, in which multiple MLO stations share SLA status information to prioritize flows that are at risk of not meeting their SLAs. Finally, a Graph-driven Min-Max Link Scheduling (GMMLS) framework is presented for dense multi-AP deployments with overlapping basic service sets, coordinating MLO link usage across APs to minimize maximum SLA stress in the network. NS-3-based simulation results in industrial-like scenarios show that the proposed mechanisms significantly improve SLA compliance (up to ≈90% reduction in SLA deviation).However, these MLO-based mechanisms require MAC-level support in Wi-Fi chipsets and are therefore tightly coupled to vendor implementations and future Wi-Fi 7/8 hardware. To complement this long-term direction with a solution that can be deployed immediately on existing Wi-Fi infrastructure, the second part of the thesis turns to TSN mechanisms operating above the MAC layer.The second part of the thesis evaluates TSN FRER for safety-critical applications such as Open Platform Communications Unified Architecture (OPC UA) Safety traffic over Wi-Fi. Using an emulation framework that couples real OPC UA Safety stacks with Wi-Fi models, the thesis shows that FRER over disjoint WLAN paths can drastically reduce safety-message round-trip times and raise communication availability from nearly zero in a single-WLAN configuration to above 90% under high load, at the expense of significant spectrum overhead. As a next step to address this overhead, an SLA-Aware Adaptive Redundancy (SAAR) scheduler is introduced, which dynamically activates FRER only when required to protect the application’s SLA, defined in terms of delay bounds and loss/SLA violation constraints. SAAR uses application-level feedback (e.g., OPC UA Safety round-trip times and availability) and channel utilization to decide when and where to replicate packets, preserving safety-grade reliability while cutting replication overhead by nearly two orders of magnitude compared to static FRER. Overall, this thesis charts a path from today’s best-effort wireless links to tomorrow’s SLA-guaranteed industrial Wi-Fi, unifying safety-critical redundancy, multi-link operation, and multi-AP coordination into a coherent toolkit for heterogeneous IIoT requirements.
  

DOCTORAL DEGREE IN PHOTONICS

• ABDULLAH, SAAD: Extreme nanophotonics architectures for the control of light at deep-subwavelength scales
  
  Author: ABDULLAH, SAAD
  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: 20/02/2026
  Reading date: pending
  Reading time: pending
  Reading place: pending
  Thesis director: GARCÍA DE ABAJO, JAVIER | MKHITARYAN, VAHAGN
  Thesis abstract: Technological progress in the twenty first century increasingly relies on the ability to control light and electromagnetic fields across multiple spatial and spectral scales. As device dimensions shrink, strong confinement becomes essential, and plasmons, the collective oscillations of conduction electrons in metals, provide an efficient route to concentrate optical fields far below the diffraction limit and enhance light matter interactions. In this thesis, we engineer and study strongly confined optical fields in nanoengineered metallic systems, where plasmonic resonances play a central role in shaping both linear response and nonlinear emission.In the first part, we study scatterer assisted coupling of free space radiation into surface supported plasmonic modes, which cannot be efficiently excited by direct illumination due to strong momentum mismatch. We show that metallic nanodisks placed near a plasmon supporting interface can launch surface plasmons, with coupling governed by disk position and distance from the surface. By tuning the nanodisk size, we control its resonance wavelength and access a wide near infrared to infrared spectral region. We further investigate periodic nanodisk arrays, where lattice resonances reshape the scattering response and shift the optimal scatterer to substrate distance. By tuning the array geometry, we identify configurations that maximize coupling and determine optimal launching conditions. Results are supported by analytical modelling and numerical simulations.The second part focuses on ultrathin epitaxial crystalline silver films with thicknesses of only a few tens of monolayers, approaching the monolayer limit. These films provide strong intrinsic confinement and support high quality resonances over a broad spectral range spanning the visible and near infrared. We investigate patterned geometries including ribbons, nanotriangles, bow tie antennas, and rods, and demonstrate robust tunability with quality factors approaching, and for optimal configurations reaching, values on the order of 11. We compare pre patterned and post patterned fabrication approaches and show that suitable capping layers suppress dewetting and ensure long term stability. Despite their reduced thickness, we show that their optical response can be described using a modified Drude model with increased damping to account for confinement and fabrication related losses.Finally, we investigate the nonlinear optical response of these ultrathin metal systems with emphasis on second harmonic generation. In ultrathin crystalline films, strong interfacial symmetry breaking and vertical confinement enable a measurable nonlinear response. We show that decreasing film thickness enhances the second harmonic signal, and that resonant nanopatterning, particularly using stable capped silver nanoribbon arrays, yields broad tunability and strong plasmon enhanced near fields, producing conversion efficiencies a few orders of magnitude higher than planar films under resonant excitation.Overall, this work highlights plasmon enabled nanoengineering as a powerful route to control and concentrate light beyond the diffraction limit, enabling compact nonlinear photonic architectures, silicon compatible frequency conversion, and emerging quantum photonic technologies.
  
• CAGETTI, MARTA: Integrated Charge Sensing and Electromechanics in Suspended Carbon Nanotube Quantum Devices
  
  Author: CAGETTI, MARTA
  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: 11/02/2026
  Reading date: 17/03/2026
  Reading time: 15:00
  Reading place: Elements Room & Teams: Meeting ID: 382 823 036 636 54 Passcode: rd7ja2kU
  Thesis director: BACHTOLD, ADRIAN | FORSTNER, STEFAN
  Thesis abstract: In this thesis we present an ultrasensitive, fast and widely tunable charge detection architecture, suitable for the readout of both electronic states and mechanical motion. The platform is implemented in suspended carbon nanotube (CNT) devices. Our approach employs a radiofrequency (RF) readout scheme operating without impedance matching, thereby avoiding one of the main practical limitations of conventional reflectometry. The readout achieves charge sensitivities exceeding state of the art RF detection techniques, while relying on a comparatively simple measurement setup. The device is based on an integrated single nanotube platform, in which a system of gate defined quantum dots and a proximal quantum dot based charge sensor are hosted in the same suspended CNT and separated by a short metallic drain electrode. The drain is connected to an RLC resonator with a resonance frequency fRLC approximately 1.25 MHz and a bandwidth of 50 kHz, enabling RF readout of the charge sensor current at the circuit resonance. This geometry provides strong capacitive coupling while maintaining independent electrostatic control of the sensor operating point and of the target quantum dots. Using this platform, we achieve self charge sensitivities of order 10^-7 e/sqrt(Hz) and an exceptionally low single shot infidelity, 1 - F approximately 10^-15, for an integration time tau approximately 3.5 microseconds. Beyond the readout of electronic charge transitions in the target quantum dots, the same charge sensor provides highly sensitive access to the mechanical degrees of freedom of the suspended nanotube in the system region. Mechanical displacement is transduced into variations of the charge sensor quantum dot conductance, enabling measurements ranging from driven nonlinear dynamics to thermomechanical motion in the few phonon regime. Crucially, our platform allows operation in a regime where electromechanical backaction, which is typical of suspended carbon nanotubes hosting quantum dots, is strongly suppressed. This addresses one of the central challenges of CNT based nanomechanics: in single dot electromechanical architectures, achieving strong or ultrastrong coupling generally requires operation near charge degeneracy, where coupling to electronic reservoirs and stochastic tunneling lead to excessive dissipation, frequency noise and a pronounced reduction of the mechanical quality factor. Indeed, in previous experiments in the ultrastrong coupling regime, measurement backaction broadened the mechanical response to the point of obscuring access to the intrinsic mechanical properties. In contrast, in our devices we maintain high readout sensitivity without any observable degradation of the mechanical quality factor Q, enabling quantitative spectroscopy of the resonator while preserving its intrinsic mechanical properties. This capability to perform quantitative spectroscopy of a nanomechanical resonator coupled to a two level system in the few phonon regime constitutes a key requirement for advancing towards experiments in the quantum regime, where preserving intrinsic mechanical coherence is essential. The high degree of tunability of our platform enables precise control of charge occupation, tunnel couplings and electrostatic potentials, allowing systematic studies of electromechanical coupling from the single electron regime in a simple quantum dot to the double quantum dot configuration. We demonstrate ultrastrong electromechanical coupling, opening the door to future work on nonlinear nanomechanics, mechanical qubits, quantum delocalization and carbon nanotube based quantum simulation.
  

DOCTORAL DEGREE IN POLYMERS AND BIOPOLYMERS

• CASTREJON COMAS, VICTOR: Electroresponsive hyaluronic acid-based hydrogel for wound healing with real-time monitoring of bacterial metabolism to prevent infection
  
  Author: CASTREJON COMAS, VICTOR
  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 POLYMERS AND BIOPOLYMERS
  Department: Department of Chemical Engineering (EQ)
  Mode: Normal
  Deposit date: 11/02/2026
  Reading date: 13/03/2026
  Reading time: 11:00
  Reading place: ESCOLA D'ENGINYERIA DE BARCELONA ESTmeet.google.com/byq-scje-iatAv. Eduard Maristany, 16 Sala PolivalentPlantA 0 (A.0.3) https://eebe.upc.edu/es
  Thesis director: ALEMAN LLANSO, CARLOS ENRIQUE | PÉREZ MADRIGAL, MARIA DEL MAR
  Thesis abstract: Chronic skin wounds represent a major healthcare and socioeconomic challenge due to their high prevalence, prolonged healing times, and elevated risk of complications, particularly in conditions such as diabetic foot ulcers and vascular ulcers. In this context, the development of advanced wound dressings capable of promoting tissue regeneration while simultaneously monitoring the wound status constitutes a critical clinical need. Hyaluronic acid (HA), as a natural component of the skin extracellular matrix, stands out as an ideal biomimetic platform for the design of hydrogels intended for cutaneous wound healing, owing to its excellent biocompatibility, high water-retention capacity, and regulatory role in the wound-healing process.In this thesis, HA-based hydrogels crosslinked via click chemistry are developed using a three-arm polyethylene glycol (PEG) as a crosslinking agent, enabling the formation of well-defined three-dimensional networks under mild and biocompatible conditions. These hydrogels (clickHA) are designed to maintain a favorable moist environment, absorb wound exudate, and promote autolytic debridement, thereby contributing to the removal of necrotic tissue and to the reduction of bacterial burden. The trifunctional architecture of PEG allows precise control over the crosslinking density and, consequently, over the mechanical and structural properties of the hydrogel, ensuring adequate mechanical compliance with skin tissue and ease of clinical handling.The incorporation of a semi-interpenetrating network of a conducting polymer, poly[3,4-ethylenedioxythiophene-2-yl methanol] (PEDOT-MeOH), obtained by chemical oxidative polymerization of EDOT-MeOH monomers infiltrated into the clickHA matrix, endows the system with electro-responsive properties without compromising its structural integrity or biocompatibility. The influence of the infiltrated monomer concentration is systematically analyzed, demonstrating a progressive increase in electroactivity and the onset of electronic percolation at an EDOT-MeOH concentration of 0.05 M, above which effective conductive connectivity and reproducible electrochemical responses are achieved.Molecular dynamics simulations provide a mechanistic understanding of the internal organization of the system, revealing that EDOT-MeOH monomers aggregate into domains stabilized by π–π interactions that act as nucleation sites for polymerization, resulting in a homogeneous distribution of the conducting polymer within the HA matrix without altering its global structure. Finally, the electrochemical stability of the system and its application as an electrochemical sensor for the detection of NADH, a relevant biomarker of bacterial infection, are evaluated. ClickHA/PEDOT-MeOH hydrogels exhibit stable and selective electrochemical responses in the presence of interferents, both in model solutions and in bacterial culture media, enabling the early detection of infections. Overall, this thesis validates the clickHA/PEDOT-MeOH system as a robust multifunctional platform that integrates skin regeneration and electrochemical diagnostics, with high potential for translation into advanced smart wound dressings.
  
• COLOMBI, SAMUELE: Soft and Conductive Material Architectures for Flexible Electronics: from Hydrogels to Nanomembranes
  
  Author: COLOMBI, SAMUELE
  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 POLYMERS AND BIOPOLYMERS
  Department: Department of Chemical Engineering (EQ)
  Mode: Normal
  Deposit date: 20/02/2026
  Reading date: pending
  Reading time: pending
  Reading place: pending
  Thesis director: ALEMAN LLANSO, CARLOS ENRIQUE | GARCÍA TORRES, JOSÉ MANUEL
  Thesis abstract: Soft materials are key enablers of the next generation of flexible and multifunctional systems for applications spanning biomedicine, energy, and environmental technologies. This thesis focuses on the design, fabrication, and implementation of (nano)engineered polymeric materials with tailored properties for use in soft electronic devices, drug delivery systems, solar-driven water evaporators, and functional scaffolds for tissue engineering. To this end, three main families of materials were developed: composite alginate (Alg)-based hydrogels, composite gelatin methacrylate (GelMA)-based hydrogels, and polylactic acid (PLA)-based nanomembranes. For each material platform, structure–property–function relationships were systematically investigated across distinct biotechnological scenarios to achieve enhanced performance. Alg-based composite hydrogels were engineered as versatile, water-rich platforms through the incorporation of functional nanomaterials (such as PLA nanofibers and gold nanoparticles), enzymes, and conducting polymers (e.g., PEDOT:PSS). These systems were designed to exhibit improved mechanical robustness, controlled porosity, and tunable physicochemical and functional properties while maintaining their intrinsic biocompatibility. By modulating composition, the Alg-based hydrogels were successfully applied as drug delivery matrices for the sustained release of lactate, as soft electronic platforms for temperature and H₂O₂ sensing, and as flexible energy-storage devices. In addition, the introduction of secondary covalent crosslinking was explored as a strategy to enhance operational stability in H₂O₂ biosensing and solar-driven steam generation applications. In parallel, bilayered PANI–PLA nanomembranes incorporating aligned gold nanopillars were fabricated as free-standing, lightweight, conformable, and mechanically stable platforms for skin electronics. These nanomembranes enabled simultaneous pH and non-enzymatic NADH sensing, demonstrating their suitability for monitoring skin physiology and infection-related biomarkers. Finally, GelMA hydrogels were nanoengineered with magnetoelectric and/or graphene-based nanomaterials to develop bioactive scaffolds for cardiac tissue regeneration. The incorporation of these nanomaterials allowed precise tuning of the mechanical, electrical, magnetic, and biological properties of the hydrogels. Their ability to support cell adhesion and proliferation, combined with their capacity to sense cellular activity, highlights their potential as multifunctional scaffolds for engineered tissues and bioelectronic interfaces. Overall, this thesis demonstrates that rational materials design across multiple length scales enables the development of soft matter systems with tailored and synergistic functionalities, paving the way toward next-generation flexible electronic devices, controlled therapeutic platforms, and sustainable water-treatment technologies.
  

DOCTORAL DEGREE IN SIGNAL THEORY AND COMMUNICATIONS

• GHAFOURI JESHVAGHANI, NAVIDEH: Reinforcement Learning-Based Energy Efficient Network Orchestration, Slicing, and Resource Management for 6G Intelligent Networks
  
  Author: GHAFOURI JESHVAGHANI, NAVIDEH
  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: 16/02/2026
  Reading date: 16/03/2026
  Reading time: 09:30
  Reading place: ETSETB B3 Teleensenyament
  Thesis director: VARDAKAS, JOHN | VERIKOUKIS, CHRISTOS
  Thesis abstract: This dissertation responds to the emerging challenges of AI-driven 6G network management, aiming to devise smart, adaptive strategies that optimize network performance while reducing complexity and energy consumption. Through the lens of Reinforcement Learning, the study lays the foundation for self-organizing, cost-efficient, and scalable wireless communication systems that can meet the diverse demands of next-generation applications.
  
• KASULURU, VAISHNAVI: AI-Driven Network Service Management for Efficient and Sustainable Open-RAN Systems in 6G and Beyond
  
  Author: KASULURU, VAISHNAVI
  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: 24/02/2026
  Reading date: pending
  Reading time: pending
  Reading place: pending
  Thesis director: BLANCO BOTANA, LUIS | ZEYDAN, ENGIN
  Thesis abstract: This thesis presents an efficient and sustainable AI-driven resource management framework for next-generation Open Radio Access Network (O-RAN) in the context of the emerging 6G era. The framework operates in a cloud-native 6G environment and translates predictive intelligence into reliable, energy-aware orchestration. It combines advanced predictive modeling with optimization-based control to address challenges, like stochastic demand, multitenancy, and computational complexity in O-RAN. The predictive forecasting architecture is the core of the framework, quantifying uncertainty and interdependencies among network resources across multiple tenants. Probabilistic forecasting models generate distributions of future resource demands, enabling service providers to perform more informed and risk-aware resource orchestration in complex multitenant environments.Initially, the framework considered in this thesis considers univariate probabilistic estimators, including Simple-Feed-Forward (SFF), Deep Autoregressive Recurrent network (DeepAR), and Transformers, to predict individual resource demands and support effective provisioning in O-RAN. These models deliver efficient, agile, and uncertainty-aware resource predictions, which are integrated into a novel percentile-based orchestration strategy, Dynamic Percentile Adjustment Approach (DYNp). The proposed method dynamically adjusts the percentage to ensure efficient resource utilization in O-RAN systems. Selecting an appropriate percentile is critical for balancing resource waste and service reliability. However, univariate probabilistic estimates do not capture cross-resource interdependencies, leading to suboptimal decision-making. To address this limitation, the framework incorporates state-of-the-art multivariate probabilistic forecasting models such as Gaussian Process Vector Autoregression (GPVAR) and the Temporal Fusion Transformer (TFT). They jointly process multiple time series and provide robust estimates of future resource demands. These models effectively learn complex interdependencies among different resources and key parameter indicators across network slices and tenants. Furthermore, we have evaluated how low-rank approximation in GPVAR estimator enhances scalability and robustness by reducing the algorithm's training time. One of the main goals of this thesis is to achieve energy efficiency and effective resource management and sharing. By considering predictive intelligence together with power consumption, the proposed techniques proactively optimize the activation and deactivation of radio resources or radio units. This strategy significantly reduces power consumption while maintaining user experience and adhering to Service Level Agreement (SLA) guarantees. Furthermore, another relevant contribution of the thesis is the extension of the traditional cellular O-RAN architecture to include Cell-Free massive Multiple-Input and Multiple-Output (CF-mMIMO) networks, reflecting the architectural evolution for beyond 6G systems. This provides a scalable approach to ultra-dense, energy-efficient O-RAN deployments. Finally, the algorithm tools considered in the dissertation are implemented as modular applications to facilitate deployment across O-RAN. The cloud-native implementation of the forecasting and orchestration pipeline is a notable achievement. Each module has been containerized using Docker, and its functionality is exposed via Representational State Transfer (REST) APIs, such as Swagger. This enables the pipeline to operate as microservices, supporting flexible deployment, scalable execution and seamless integration within O-RAN. The thesis establishes a mathematical and architectural foundation for deploying AI-driven, sustainable, and energy-optimized O-RAN with uncertainty adaptation. It provides a basis for realizing intelligent, autonomous, and stable 6G networks and supports future research and industrial implementation of AI-powered O-RAN ecosystems.
  

DOCTORAL DEGREE IN STATISTICS AND OPERATIONS RESEARCH

• BARRERA GÓMEZ, JOSE ANTONIO: Extension of statistical methods for time series analysis with applications in environmental epidemiology
  
  Author: BARRERA GÓMEZ, JOSE ANTONIO
  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 STATISTICS AND OPERATIONS RESEARCH
  Department: Department of Statistics and Operations Research (EIO)
  Mode: Normal
  Deposit date: 23/12/2025
  Reading date: 25/03/2026
  Reading time: 11:00
  Reading place: Sala d'actes de la FMECampus Diagonal Sud, Edifici U. C. Pau Gargallo, 14 08028 Barcelona
  Thesis director: BASAGAÑA FLORES, XAVIER | GINEBRA MOLINS, JOSEP
  Thesis abstract: Part of research in environmental epidemiology focuses on the assessment of associations betweenthe exposure to environmental factors and health outcomes based on aggregated longitudinal data in a given population. To model such associations, time series analysis is typically used, in which informational units are time points (e.g. days or weeks). An aggregated measure of the outcome at each time point (e.g. yearly mean cognitive test score or daily mortality count) is linked to an aggregated measure of exposure to some environmental factor of interest at the same time point (e.g. yearly mean air pollution level or daily mean ambient temperature) using a suitable regression (e.g. linear or Poisson) model. In this context, this thesis develops two studies.The first study deals with collinearity. Distributed lag models (DLNMs) have been increasingly used to model delayed effects of environmental factors on health. DLNMs include as predictors the same exposure measured at different time points. Those lagged variables are often highly correlated resulting in correlations between the estimated regression coefficients corresponding to different lags, which can lead to unreliable results. We first illustrate such problems and then propose a visual diagnosis tool to assess consequences of such collinearity. Essentially, new values of the outcome are simulated under an alternative hypothetical effect of the exposure of interest. Then, the original model is fitted again but now using the simulated data. Finally, both original and new results are compared graphically to assess if unexpected results obtained in the original analysis could be driven by collinearity. The tool is implemented in the R package collin. We provide illustrative examples and a user’s guide.The second study extends the Poisson regression model in multi-zone time series analysis for a count outcome. Those models need to control for trends and seasonality, which can be done by including time-stratum indicators (e.g. unique combinations of year, month and day of week). That implies having to include in the model a typically high number of nuisance parameters that can cause computational issues in the estimation process. This problem can be avoided with the conditional Poisson regression model, by conditioning by the sum of the outcome event counts in each stratum, which results in a multinomial regression model. By doing this, the nuisance parameters do not need to be estimated while the model provides relative risks (e.g. change in the mean mortality for a given increase in air pollution concentration) that are adjusted for long-term trends and seasonality. In cases of data from different geographical zones, a two-stage modelling procedure is usually performed, first analysing each zone separately and then combining zone-specific results into a single overall measure using, for instance, meta-analysis. A one-stage analysis, by analysing simultaneously data from all geographic zones, could be performed by including a random effect at zone level. However, the available (frequentist) software for conditional Poisson regression does not allow including random effects. In this context, we propose and develop a one-stage modelling approach, which is computationally feasible, namely Bayesian conditional Poisson mixed model, to analyse time series data for a count outcome that analyses all zones simultaneously while maintaining the good properties of the two-stage analysis. Our approach is based on conditioning out by the sum of the outcome event counts in each zone-time stratum and the inclusion of a random effect to model zone-specific association of interest. In addition, our method allows for including a spatial structure of the random effects as well as considering potential overdispersion. In the study, we derive model equations and implement the modelling procedure in R. To facilitate usage, we develop illustrative examples and provide code and data.
  
• BORJA ROBALINO, RICARDO STALIN: Optimización bayesiana en técnicas machine Learning clásicas: redes neuronales y XGBoost y su aplicación como modelos predictores de diabetes en pacientes ecuatorianos
  
  Author: BORJA ROBALINO, RICARDO STALIN
  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 STATISTICS AND OPERATIONS RESEARCH
  Department: Department of Statistics and Operations Research (EIO)
  Mode: Normal
  Deposit date: 22/01/2026
  Reading date: 10/04/2026
  Reading time: 11:00
  Reading place: Dia 10 d’ abril de 2026 a les 11h del matí a la Sala de Juntes de la FIB
  Thesis director: MONLEON GETINO, ANTONIO | GIBERT OLIVERAS, CARINA
  Thesis abstract: Machine learning (ML) is a branch of artificial intelligence that allows human capabilities to be imitated through various algorithms and techniques that learn from data using learning processes (supervised, unsupervised, or reinforcement) for decision-making with minimal human intervention. Classic ML models have generated great results in the automation of classification and regression processes in various areas. Within classification, artificial neural networks (ANN) have gained relevance due to their ability to learn and model complex nonlinear relationships. Similarly, the XGBoost model based on decision trees has demonstrated great efficiency, speed, scalability, and performance, winning several competitions. On the other hand, Bayesian inference has provided a probabilistic and revolutionary framework for optimizing machine learning models, with the implementation of uncertainty in the estimation process, combining evidence with prior beliefs, in order to reduce overfitting and improve predictions by adjusting parameters and hyperparameters.This research aims to optimize two classic machine learning techniques (artificial neural networks and XGBoost) for classification using Bayesian inference and to build a diabetes prevention model for the Ecuadorian population. The study begins with a theoretical and mathematical conceptualization of each algorithm, followed by an analysis of the various points of intervention, programming, and implementation of Bayesian models using Markov chain Monte Carlo (MCMC) estimation techniques and variational inference (VI), validation using public databases, implementation of a client-server system with multiple specialized backends, and, finally, the development of a real application as predictors of type 1, type 2, and gestational diabetes.As a result, a Bayesian model was implemented in artificial neural networks (ANN) at two inference points. The first adjusted the parameters at each backpropagation step; however, it presented itself as an option with a prohibitive computational overhead. As a second intervention, an adjustment was made to the activation function in the final layer, obtaining positive and computationally viable results. In the case of XGBoost, the predictions were adjusted at each boosting step before vectorization, demonstrating high predictive power in both the use of the MCMC technique and IV. Validation with the Pima Indians Diabetes database and the use of various distribution functions demonstrated the robustness and sensitivity of the implemented models, while generalization and consistency were verified through application to various databases. In all cases, results superior to or equal to those obtained using the traditional model were obtained, depending on the characteristics of the data.In addition, a web application (client-server) was implemented with Bayesian proposals, allowing users to interact with the models in an easy and intuitive way, with options for data loading, parameter configuration and probability distributions, estimation techniques (MCMC or IV), training-validation process or use of cross-validation, real-time results, and model download options. The application of the Bayesian proposal to a real case, such as the prediction of type 1, type 2, and gestational diabetes, with data from Ecuadorian patients, presented encouraging results (accuracy = 99.47%), becoming the first predictive model for the three types of diabetes at the regional and national level, confirming that the use of this approach is an excellent alternative for the optimization of machine learning models.
  

DOCTORAL DEGREE IN SUSTAINABILITY

• RAMIREZ RODRIGUEZ, MARIA SARAY: Fisheries Sustainability: Environmental, Economic, and Social Vulnerabilities, Addressing the Problem Through Food Waste
  
  Author: RAMIREZ RODRIGUEZ, MARIA SARAY
  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: 11/02/2026
  Reading date: 16/03/2026
  Reading time: 10:30
  Reading place: Sala de Graus del EEABB (Campus del Baix Llobregat)
  Thesis director: REIG PUIG, MARIA LOURDES | FERNÁNDEZ GUERRERO, DAVID
  Thesis abstract: Food security and a resilient food system are aims to ensure the future of humanity in a scenario of several threats, such as climate change, the decreasing trend in available resources, the emergence of virulent diseases, global conflicts affecting supply chains, etc. Providing solutions to these issues is so vital that within the Sustainable Development Goals (SDG), sustainable production and consumption is a stated goal (nº 12), and the reduction of Food Losses and Waste (FLW) has become one of the tools for achieving this goal (nº12.3). . This study aims to contribute in FLW reduction for the fish sector, decreasing its negative impacts and increasing the sustainability of the fish sector activity. Moreover, the goal of the thesis is also to reduce fish sector vulnerability, by identifying weak points and possible areas of improvements with a focus on economic and social sustainability, but without forgetting an environmental approach.The methodology combines qualitative and quantitative methods. Public databases were used but also own data was generated by including fish sector stakeholders in the study’s development by surveys, interviews and participatory dynamics. Spain was chosen for the geographical scope due to several factors, such as its high volume of catches, consumption and international trade in fishery and aquaculture products, both in the European and global contextThe thesis is structured in the following way. Chapter 1 was developed as a diagnosis of FLW in the fisheries sector. FLW were quantified and causes, and prevention measures were detected through the use of a standardized methodology. Furthermore, two species were selected to represent Spanish fish sector based on consumption and catches. Anchovy (Engraulis encrasicolus) was chosen to represent oily fish and Hake (Merluccius merluccius) for representing lean fish. As a result, we provided FLW estimations using standardized methodologies, which allows for comparison with future research. Furthermore, our contribution enables the formulation of mitigation and prevention actions. After the diagnosis stage, interventions were designed and tested, trying to approach the problems identified in the diagnosis stage.Chapter 2 tries to improve the resilience of the fisheries sector in Catalonia by developing a market intervention for the expansion of target species. Priority was given to local species, favored by climate change, and which are currently being wasted both nutritionally and economically, due to their low commercial interest. The selected species was Sardinella aurita, which was also validated by local fishermen as a good candidate for the market intervention. The market intervention contributes to increase the economic value of catches by offering alternative products made from low-value species. Moreover, it approached consumers toward local species by adding value to proximity and offering more convenient consumption methods for unskilled consumers. On the other hand, in Chapter 3 another mitigation action was proposed in order to reduce FLW and the economic devaluation of products due to consumer concerns about food safety and aesthetic reasons. As was detected during the diagnostic stage, Anisakis negatively affects the fish sector. It is necessary to inform consumers correctly by developing communication tools that focus on risk avoidance and the social and health benefits of consuming fish. This chapter proposed and tested communications to decrease the perception of food risk towards Anisakis and increase fish consumption.Finally, recommendations have been formulated for each stage of the fish FSC to reduce FLW and increase economic profit, as well as to propose strategies to promote sector’s resilience. These recommendations include strong collaboration and better communication between stages and withpolicymakers, as well as promoting fish culture to increase consumption and reduce food safety concerns.
  
• VALLEJOS CARTES, ROSANA: Examinando preferencias, motivaciones y actitudes de los consumidores para mejorar la sostenibilidad de los sistemas agroalimentarios. Una aplicación a los sistemas agropastorales extensivos.
  
  Author: VALLEJOS CARTES, ROSANA
  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: 22/12/2025
  Reading date: 20/03/2026
  Reading time: 12:30
  Reading place: Salón de grados de la EEABB en Castelldefels
  Thesis director: GIL ROIG, JOSE MARIA
  Thesis abstract: In the context of global transformations in the agri-food system—marked by environmental pressures, biodiversity loss, market concentration, and dietary homogenization—a critical scenario for sustainability has emerged. Within this landscape, extensive livestock farming, based on grazing and traditional management practices, constitutes a viable alternative to intensive production by integrating ecological, economic, and sociocultural dimensions. Nevertheless, its long-term viability depends largely on consumer recognition, as purchasing decisions can drive the transition toward more sustainable systems. This thesis assumes that consumers operate as agents of change but face constraints linked to limited information, perceptions of high prices, and insufficient market differentiation. Accordingly, the general objective was to assess the economic and social viability of extensive livestock systems through an examination of consumer preferences, motivations, and attitudes toward sustainably produced lamb meat, generating evidence to inform differentiation strategies and public policies that support agri-food sustainability. The research employed a mixed-methods design developed in two complementary phases. The qualitative phase comprised semi-structured interviews and participatory workshops with sheep producers to identify sustainability drivers and market-valued attributes. The quantitative phase implemented Discrete Choice Experiments (DCEs) among consumers in Catalonia, carried out in two stages: an exploratory study (n = 396) to refine attributes and optimize the experimental design, followed by a larger study (n = 1,003) incorporating attitudinal measures through the New Ecological Paradigm (NEP) scale. Data were analyzed using mixed logit and latent class models, enabling the identification of heterogeneous preferences, the estimation of willingness to pay, and the integration of environmental and ethical attitudes into consumer choice modeling.The findings reveal a complex interaction between knowledge, attitudes, and values in shaping preferences for sustainable meat. Results indicate limited consumer knowledge of production systems—particularly extensive livestock farming—despite the strong symbolic valuation of local origin, animal welfare, and territorial authenticity. Labeling and certification mechanisms emerge as essential tools for building trust and supporting informed decisions. The highest willingness to pay is associated with organic production and animal welfare, while sensory cues such as color and visible fat exert a complementary influence.The research confirms a persistent gap between stated attitudes and actual behaviors, shaped by economic constraints, purchasing routines, and information availability. However, it also identifies a segment of consumers who are informed and value-consistent, suggesting opportunities for differentiation through targeted communication, certification, and education initiatives. Overall, the thesis provides an integrated understanding of the relationship between consumer behavior and sustainability, advocating for a renewed appreciation of agro-pastoral systems as public goods that deliver not only food but also essential ecosystem and cultural services. Its results contribute to the development of agri-food policies and market strategies that acknowledge the role of consumer demand in advancing sustainable production models, reinforcing consumers as central actors in the transition toward an ethical, territorially grounded, and environmentally responsible agri-food economy.
  

DOCTORAL DEGREE IN THEORY AND HISTORY OF ARCHITECTURE

• IAMPIERI, ARIANNA: Oriol Maspons, un archivio della memoria visiva del secondo ‘900. La fotografia come interpretazione del contesto abitato
  
  Author: IAMPIERI, ARIANNA
  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 THEORY AND HISTORY OF ARCHITECTURE
  Department: Department of History and Theory of Architecture and Communication Techniques (THATC)
  Mode: Normal
  Deposit date: 13/01/2026
  Reading date: 25/03/2026
  Reading time: 11:00
  Reading place: ETSAB (Escuela Técnica Superior de Arquitectura de Barcelona) - Planta Baja - Sala de GradosAv. Diagonal, 649-651 - 08028 - Barcelona
  Thesis director: PIZZA DE NANNO, ANTONIO | BERGERA SERRANO, JOSE IGNACIO
  Thesis abstract: Oriol Maspons was one of the leading figures of the photographic scene in Barcelona during the second half of the 20th century and a key participant in the renewal of photographic language, which at the time was still anchored in academic and traditionalist canons. Although his vast and eclectic work has been studied over time, his contribution to the representation of architectural and urban space remains largely unexplored, despite this field being a constant throughout his photographic career. This thesis aims to delve into Oriol Maspons’s perspective on the inhabited environment, thereby contributing to the fascinating study of the intersections between photography, architecture, and urban space.Maspons's interest in architecture and the city emerged from the early years of his amateur practice and solidified over time, as demonstrated by the numerous professional collaborations he undertook, sometimes independently, sometimes together with his associate Julio Ubiña, with whom he founded a photography studio in 1957. Maspons and Ubiña produced photo reports for several architects and editorial projects in the field, including Cuadernos de Arquitectura (the journal of the Col·legi d’Arquitectes de Catalunya - COAC), documenting major architectural works and the ongoing urban transformation; another significant project was the work he carried out for the volume Arquitectura Española Contemporánea by Lluís Domènech i Girbau.In addition to being a visual witness to the architectural production of his time, Maspons also turned his camera toward historical heritage, capturing modernist and Gothic architecture, the latter being the focus of the photographic book Arquitectura gótica catalana, featuring texts by Alexandre Cirici and graphic design by architects Oscar Tusquets and Lluís Clotet.In parallel, Maspons visually narrated his city, Barcelona, through tourist guides such as Això també és Barcelona (in collaboration with Ubiña) and Barcelona pam a pam, contributing to the dissemination of a previously unseen image of the city. During the Olympic period, he worked alongside other photographers such as Francesc Català-Roca, Colita, and Xavier Miserachs, offering valuable testimony to the radical architectural and urban transformations that reshaped Barcelona in the 1980s.These examples highlight both the scope of Maspons’s work and his versatility. A deeper study of his oeuvre, which deserves greater attention, represents a significant contribution to both the history of photography and the history of Spanish architecture in the second half of the 20th century, given Maspons’s important role in the architectural discourse of the time and in shaping the visual imagination of the Catalan capital.Through the analysis of existing bibliography, the study of unpublished archival material, and the examination of publications illustrated with his photographs, the aim is to offer a broader and more in-depth perspective on the subject. The objective is to expand existing knowledge with new findings, consolidate certain positions, develop critical reflections on Maspons’s work in architectural and urban representation, and open up new research fields.
  
• ROGER GONCE, JOAN: “El barrio que (nos) construimos” Creixement i desenvolupament urbà del barri de Roquetes de Barcelona, a través del Padró Municipal d’Habitants (1940-1978)
  
  Author: ROGER GONCE, JOAN
  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 THEORY AND HISTORY OF ARCHITECTURE
  Department: Department of History and Theory of Architecture and Communication Techniques (THATC)
  Mode: Normal
  Deposit date: 13/01/2026
  Reading date: 27/03/2026
  Reading time: 11:00
  Reading place: ETSAB (Escuela Técnica Superior de Arquitectura de Barcelona) - Planta Baja - Sala de GradosAv. Diagonal, 649-651 - 08028 - Barcelona
  Thesis director: ROSSELLO NICOLAU, MARÍA ISABEL | OYON BAÑALES, JOSE LUIS
  Thesis abstract: This study addresses the urban history of the Roquetes neighborhood of Barcelona during the Franco period, with the aim of analyzing its formation, consolidation and transformation over the course of more than forty years of dictatorship. The work aims to provide data and a critical reflection on the social, economic and urban processes that shaped this working-class and markedly immigrant neighborhood, in a context of accelerated growth, precarious infrastructure and territorial inequalities.The meticulous analysis of the municipal population register, systematically cross-referenced with other demographic, labor and urban sources, has allowed us to delve deeper into key issues for understanding the neighborhood's trajectory: the migratory networks and chains that sustained its growth; the forms of work and the opportunities —or limits— of social mobility for its residents; the housing conditions and models of urban production; and, finally, the construction of the neighborhood as a space for coexistence, identity and sociability in a framework of institutional abandonment and neighborhood responses.Through this combination of perspectives and sources, the research provides an integrated look at Roquetes that contributes to the broader debate on urban peripheries, Franco's socialist regime and the everyday experiences of popular sectors in 20th-century Barcelona.
  
• ZIAIEBIGDELI, MOHAMMADAMIN: Computing quality in housing: examining the evolution and systematization in computational design in architectural plan design and analysis
  
  Author: ZIAIEBIGDELI, MOHAMMADAMIN
  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 THEORY AND HISTORY OF ARCHITECTURE
  Department: Department of History and Theory of Architecture and Communication Techniques (THATC)
  Mode: Normal
  Deposit date: 19/02/2026
  Reading date: pending
  Reading time: pending
  Reading place: pending
  Thesis director: ROSSELLO NICOLAU, MARÍA ISABEL | HERNÁNDEZ FALAGÁN, DAVID
  Thesis abstract: This research undertakes a meticulous exploration into the systematization and analysis of architectural plans, tracing their evolution through the lens of computational design thinking from the 20th century to the present. The specific context is living spaces, where the quality and functionality are intrinsically tied to the systematic extraction and application of design parameters. This dissertation explores the evolution of computational thinking in architecture by bridging the theoretical paradigms introduced by pioneers Alexander Klein, Cedric Price, Christopher Alexander, and Nicholas Negroponte with contemporary design practices. Through an inductive methodology, three case studies are analyzed to demonstrate how computational tools and technologies enhance spatial quality, adaptability, and user engagement. Each case study corresponds to the key indicators established by these pioneers—spatial geometry, temporal adaptability, participatory design, and digital computation—illustrating their enduring influence on modern architecture. By mapping these foundational ideas to advanced practices such as parametric modeling, smart systems, and digital fabrication, this research highlights the practical relevance of these paradigms in addressing contemporary architectural challenges and shaping innovative, human-centered spaces.The journey begins with Alexander Klein’s Existenzminimum concept in the 1920s-1930s. His scientific approach to design emphasized space optimization and the systematization of plans using both qualitative and quantitative parameters, laying the groundwork for future computational design methods. As we move into the 1960s, we encounter Christopher Alexander’s ‘Pattern Language.’ Alexander brought a new perspective to plan systematization, employing rules and systems to extract design parameters and analyze architectural planning. His methodologies form a vital cornerstone of computational design as we know it today.In the 1970s, Cedric Price introduced a more dynamic approach. His vision of architecture, as exemplified in the 24 hour living toy project, accommodated changing user needs and behaviors. His focus on user-centric parameters marked a shift in computational design thinking towards adaptability and flexibility. Presently, computational design thinking in the context of living spaces aims to synthesize the lessons from these key figures, emphasizing the analysis and extraction of design parameters. These parameters, enriched by the advancements in technology and the increasingly user-centric approach, are utilized to optimize and improve the quality of living spaces This research’s trajectory, from past methodologies to contemporary practices, provides an in-depth understanding of how computational thinking and plan analysis have evolved. By extracting and studying the diverse parameters used across time, this research aims to enrich the computational design discourse and advance the design of living spaces.
  

DOCTORAL DEGREE IN ARCHITECTURAL, BUILDING CONSTRUCTION AND URBANISM TECHNOLOGY

• ARIAS CUEVAS, JOSÉ GABRIEL: Proyectos de recuperación de zonas vulnerables con materiales de ciclo cerrado. Casos de estudio, proyectos URBE.
  
  Author: ARIAS CUEVAS, JOSÉ GABRIEL
  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: 05/03/2026
  Deposit END date: 18/03/2026
  Thesis director: BOSCH GONZÁLEZ, MONTSERRAT
  Thesis abstract: The Dominican Republic, specifically Santo Domingo, has faced decades of unplanned urbanization in highly vulnerable areas, such as the banks of the Ozama River. This generates a socio-environmental risk intensified by overpopulation and a lack of specific public policies. Simultaneously, the absence of an integrated system for managing Construction and Demolition Waste (CDW), often disposed of improperly, causes critical environmental impacts. This research addresses the integration of CDW management and closed-loop materials into urban redevelopment projects promoted by the State in critical areas. Using La Nueva Barquita and Domingo Savio (URBE Projects) as case studies, the research serves as a starting point for future interventions in the country.The central objective is to propose construction alternatives to existing ones for urbanization projects in flood-prone areas through the systematic use of closed-loop materials and CDW recovery and valorization systems. It seeks to provide strategic knowledge so that these urban interventions can become "waste sinks," driving sustainable, socially committed, and viable development.The research follows a mixed methodology combining: a documentary study and critical analysis of local regulations; international references; and the state of the art regarding vulnerability and river intervention projects. It includes the analysis of the case study settlements, the construction sector and its main stakeholders, and the regulatory framework, alongside successful experiences of urban interventions using recycled products. Field research involved site visits and surveys of both residents of the Nueva Barquita project and key stakeholders in Santo Domingo's construction sector. Finally, a technical-economic analysis of work items was conducted using quantification tools such as TCQ/BEDEC. This triple approach allowed for a comprehensive understanding of local barriers and opportunities.The results are framed within three transformation vectors:Regulatory/Institutional Vector: Although a legal framework exists, technical instruments and specific contractual clauses are needed to integrate CDW management as a mandatory requirement in public works.Socio-Economic Vector: The research identified an active informal reuse market, demonstrating latent demand. Formalizing this sector can generate a new competitive and formalized economic fabric.Technical/Territorial Vector: The real feasibility of replacing conventional materials with CDW in redevelopment projects is validated, optimizing resilience on riverbanks through solutions combined with nature-based solutions (NBS).The findings demonstrate that while the Dominican State has a visible social commitment to relocating vulnerable populations, a critical gap persists in regulatory application and technical CDW integration. The main contribution of this thesis lies in having integrated a technical, regulatory, and territorial diagnosis that validates the hypothesis that urban projects can function as "waste sinks" in the Dominican context.The research establishes technical, institutional, and market foundations for the Dominican Republic to adopt a circular economy model in construction. The sector's robust growth presents an exceptional opportunity to implement this model, attracting sustainable investment and strengthening international competitiveness. This transforms the waste challenge into a strategic lever for resilient urban development and national economic prosperity.
  

DOCTORAL DEGREE IN AUTOMATIC CONTROL, ROBOTICS AND VISION

• AGUILAR PLAZAOLA, JOSÉ AGUSTÍN: DATA-DRIVEN MODELLING, STATE ESTIMATION, CELL CONTROL AND MOTION PLANNING FOR PEM FUEL CELL-POWERED VEHICLES
  
  Author: AGUILAR PLAZAOLA, JOSÉ 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 AUTOMATIC CONTROL, ROBOTICS AND VISION
  Department: Institute of Robotics and Industrial Informatics (IRI)
  Mode: Normal
  Deposit date: 05/03/2026
  Deposit END date: 18/03/2026
  Thesis director: HUSAR, ATTILA PETER | ANDRADE CETTO, JUAN
  Thesis abstract: This doctoral thesis presents novel advances in the areas of modeling, state estimation, path planning, and control to improve energy efficiency and durability of the powertrain of autonomous robots and electric vehicles driven by proton exchange membrane fuel cells. The main objective of the present work is to design and implement algorithms that, based on a thorough knowledge of the systems in question, improve the characteristics and outperform the state-of-the-art methods. Special emphasis is placed on testing the developed algorithms, as much as possible, with dynamic experimental profile dataIn the area of proton-exchange membrane (PEM) fuel cell modelling, a computationally efficient physical model is proposed. Next, a model with a structure based on neural networks, built exclusively from data, is developed and validated. This model is framed within a new paradigm of machine learning, the computation by reservoirs. Subsequently, a hybrid model is built, combining both the physical model and the data-driven model by means of a fusion algorithm based on radial basis functions. The three models are tested with a set of dynamic experimental data, and it is shown how the proposed hybrid structure outperforms each of the individual models.In the area of state estimation, a particle filter is developed with the objective of estimating internal states (or parameters) of the fuel cell, taking into account the nonlinearity of the system and the uncertainty in its model. The algorithm is capable of estimating the internal variables of a nonlinear system with non-Gaussian probabilistic distribution. The algorithm is implemented to estimate the exchange current density of a fuel cell and is tested with two sets of experimental data, outperforming two state-of-the-art estimation algorithms. The exchange current density estimation is then used to fit an auto-regressive model and predict the evolution of the stack voltage in a durability experiment.In the area of PEM fuel cell control, an architecture composed of a high-level controller is proposed, which is in charge of calculating optimal temperature values with the objective of minimizing the degradation of the catalyst layer of the PEM fuel cell and at the same time maximizing its performance. These optimum values are then sent to the local controller of the fuel cell temperature regulation system. The proposed controller is based on the model predictive control paradigm; for this, a multiobjective cost function is designed, based on state-of-the-art models of the platinum degradation process that occurs during stack operation. The controller is validated in simulation tests and shown how it can adapt the temperature according to load conditions, optimizing the performance of the catalyst layer and minimizing its degradation.In the area of path planning, a new planning algorithm is developed taking into consideration the degradation mechanisms in the catalyst caused by the cell voltage profile. The developed algorithm is an extension of the A* algorithm, including new cost and heuristic functions based on the latest degradation models available in the literature. These functions incorporate penalties related to the expected voltage profile in the routes that are more detrimental to the catalyst integrity. Simulation tests are performed with different scenarios and the performance of the developed path planner is compared with the conventional A* algorithm.In the area of energy efficiency control, a controller is developed with the objective of including energy optimization in an adaptive cruise control module. Each part of the controller is designed, including the system model, the cost function, and the constraints. A series of simulation tests are performed to compare the performance between the energy-optimized adaptive cruise controller and the conventional one.
  

DOCTORAL DEGREE IN BIOMEDICAL ENGINEERING

• BARRANCO ALTIRRIBA, MARIA: Computational metabolomics to uncover alterations in diabetes mellitus and its complications
  
  Author: BARRANCO ALTIRRIBA, MARIA
  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 BIOMEDICAL ENGINEERING
  Department: Department of Automatic Control (ESAII)
  Mode: Article-based thesis
  Deposit date: 12/03/2026
  Deposit END date: 25/03/2026
  Thesis director: PERERA LLUNA, ALEXANDRE | MAURICIO PUENTE, DIDAC
  Thesis abstract: Metabolomics is the discipline focused on the study of the metabolome, defined as the complete set of small molecules present in a biological system. Metabolites play key roles in cellular processes and interact with other omic layers such as the genome, transcriptome, and proteome. For these reasons, metabolomics provides a powerful approach to uncover underlying biological mechanisms related to specific medical conditions or environmental exposures. Lipidomics, a subfield of metabolomics, focuses on the lipidome, the full lipid content of a system. Metabolomics has shown great potential in understanding metabolic diseases, a growing public health concern that includes both type 1 (T1D) and type 2 diabetes (T2D). T1D is the most prevalent form of diabetes in childhood, while T2D is more frequently acquired during adulthood, and its incidence is increasing at an alarming rate. Both forms can lead to serious microvascular and macrovascular complications.Analytical techniques such as liquid chromatography coupled with mass spectrometry (LC-MS) enable metabolome characterization. Untargeted metabolomics aims to detect the widest possible range of metabolites. However, the resulting data are complex and require careful handling, highlighting the importance of computational metabolomics for preprocessing, filtering, analysis, and interpretation. Interpretation remains the most challenging stage, encompassing LC-MS annotation and enrichment analysis. In lipidomics, the lack of lipid-specific pathway databases further complicates this step.This thesis is broadly divided into two parts: one focusing on computational metabolomics and the other on the more specific field of computational lipidomics. In the first part, diffusion in biological knowledge-based graphs is shown to support LC-MS annotation by prioritizing potential candidates. Building on this approach, the mWISE R package is proposed for the annotation of LC-MS data. mWISE is then applied to annotate a diabetes-related dataset, and the resulting annotations are used to perform pathway enrichment analysis. This analysis reveals the association between T2D incidence and guanine, pregnenolone sulfate, and citrulline, as well as nucleotide metabolism and ABC transporter pathways.In the lipidomics section, a broad panel of disrupted lipids in T1D and T2D is identified. Additionally, sex-specific differences are uncovered, along with lipids that show progressive changes from normoglycemia to prediabetes and T2D. A set of lipids associated with T2D exclusively in women, and others linked to T1D only in men, are proposed to contribute to sex-specific differences in the prevalence of diabetes-related complications. In the same population, a disrupted lipid profile associated with subclinical carotid atherosclerosis is identified exclusively in individuals with T2D. These lipids are hypothesized to underlie the more severe progression of atherosclerosis in T2D through specific molecular mechanisms. Moreover, this atherosclerosis-associated lipid alteration is more pronounced in individuals with T2D who are current or former smokers, as well as in those not receiving lipid-lowering treatment. Finally, a functional enrichment algorithm for lipidomics data is developed and used to interpret both lipidomics studies. The algorithm is based on molecular embeddings of lipids, obtained through a language model that was benchmarked against similar models. The enrichment results strongly align with previous interpretations and additionally highlight an overlooked relationship between T1D and reduced levels of docosahexaenoic acid.
  

DOCTORAL DEGREE IN COMPUTER ARCHITECTURE

• VALDÉS JIMÉNEZ, ALEJANDRO MAURICIO: Design, parallelization and acceleration of algorithms to discover three-dimensional patterns in proteins
  
  Author: VALDÉS JIMÉNEZ, ALEJANDRO MAURICIO
  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: 03/03/2026
  Deposit END date: 16/03/2026
  Thesis director: JIMENEZ GONZALEZ, DANIEL | NUÑEZ VIVANCO, GABRIEL
  Thesis abstract: The rapid growth of protein structure databases, such as the Protein Data Bank (over 230,000 structures) and AlphaFold (over 200 million structures), requires efficient and scalable algorithms capable of exploiting high-performance computing (HPC) architectures to enable large-scale structural analysis in reasonable times. This thesis focuses on the design and implementation of optimized and parallel algorithms for discovering, analyzing, and clustering conserved three-dimensional amino acid patterns in proteins. The work focuses on the Geomfinder algorithm (A multi-feature identifier of similar three-dimensional protein patterns: a ligand-independent approach), which compares three-dimensional patterns between pairs of proteins, and the novel 3D-PP algorithm (A tool for discovering conserved three-dimensional protein patterns), proposed in this thesis, which discovers and clusters common three-dimensional patterns within protein sets. Both algorithms are ligand and sequence independent and do not require predefined patterns, enabling the identification of previously unknown functional sites. However, their original sequential implementations limit their applicability to large datasets. For Geomfinder, several sequential optimizations were introduced to reduce algorithmic complexity and long-latency operations. The incorporation of a Merge Join–based strategy reduced partial scoring complexity from O(N×M) to O(N+M), ensuring each descriptor element is evaluated only once. Lazy evaluation and reordering of partial scoring function calls further reduced execution time. These optimizations achieved speedups ranging from 6.2x to 19.7x, depending on the search range. Multiple parallelization strategies were then explored, including OpenMP, MPI, hybrid MPI+OpenMP, and CUDA. OpenMP with fine-grained data decomposition and optimized scheduling achieved near-ideal speedups, reaching 32.6x with 64 threads. MPI-based distributed parallelization achieved up to 19.4x speedup with 64 processes, while hybrid MPI+OpenMP further improved performance, reaching 67.4x using 1,024 threads. GPU acceleration using CUDA provided speedups of up to 8.6x, with performance increasing for larger workloads. After applying the algorithmic optimizations to the original sequential version, profiling revealed a change in the computational bottleneck, and an additional OpenMP parallelization stage was applied, achieving up to 494x acceleration over the original sequential version. In one case study, the runtime was reduced from over one hour to approximately 3.4 seconds. For the 3D-PP algorithm, profiling revealed that over 96% of execution time was spent processing protein chains. All major components were parallelized. Three OpenMP approaches were evaluated, with the best solution based on explicit and nested tasks, achieving a 22.3x speedup and reducing execution time from 1.25 hours to 201.5 seconds. Distributed-memory strategies using MPI focused on minimizing communication through early pattern reduction, achieving speedups of approximately 32x with 64 processes. Hybrid MPI+OpenMP implementations further improved performance, with the best approach achieving a 162.5x speedup and reducing runtime to 27 seconds. This hybrid approach mitigated synchronization overhead inherent to pure OpenMP implementations and demonstrated weak-scaling efficiency (90-100%) up to 8 processes, although efficiency dropped to around 72% when using 16 processes due to increased load imbalance and synchronization costs. The results show that explicit task parallelism and early data reduction substantially improve the performance and scalability of 3D-PP. All these improvements will help us address processing and pattern discovery in larger protein databases.
  

DOCTORAL DEGREE IN ELECTRICAL ENGINEERING

• JENÉ VINUESA, MARC: Data-Driven and Generative Methodologies for Enhanced Grid-Edge Visibility in Distribution Grids
  
  Author: JENÉ VINUESA, 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 ELECTRICAL ENGINEERING
  Department: Department of Electrical Engineering (DEE)
  Mode: Normal
  Deposit date: 12/03/2026
  Deposit END date: 25/03/2026
  Thesis director: ARAGÜÉS PEÑALBA, MÒNICA | SUMPER, ANDREAS
  Thesis abstract: The rapid deployment of distributed energy resources (DERs), such as residential photovoltaic (PV) systems, heat pumps, and electric vehicles, is accelerating the energy transition while reshaping electrical distribution grids. Although these technologies enable decarbonization and flexibility, their widespread installation behind the meter (BTM), together with limited measurement granularity, restricted data access, and the absence of dedicated metering, creates blind spots that hinder reliable grid operation, planning, and monitoring under high DERs penetration. This doctoral thesis addresses limited grid-edge visibility by developing data-driven methodologies to infer unobserved distribution-level phenomena from low-resolution smart meter data under realistic operational constraints. Grid-edge visibility is framed as the ability to reconstruct both legitimate and illegitimate power exchanges, encompassing BTM DERs behavior and non-technical losses (NTLs). The proposed contributions aim to support actionable and risk-aware decision-making in increasingly decentralized distribution systems. The first part of the thesis focuses on NTL detection and characterization. A comprehensive framework is proposed to identify abnormal losses through energy balances and machine learning using transformer- and customer-level active power measurements. The methodology enables fraud detection and classification, while an unsupervised customer flagging module supports targeted inspection strategies. Validation using real-world data from a Spanish distribution system operator demonstrates robust performance under realistic data availability and class imbalance. The second part addresses customer-level BTM PV disaggregation. A deterministic and adaptive hybrid methodology is introduced to detect PV installations, estimate their capacity, and disaggregate generation from net consumption measurements. By combining data-driven and physics-based models with contextually supervised source separation techniques, the approach captures system-specific and seasonal effects while remaining suitable for practical deployment. Extensive case studies demonstrate robustness to data length, seasonal variability, aggregated metering, and cross-domain generalization. Finally, the thesis extends disaggregation to a probabilistic setting using generative artificial intelligence. A conditional diffusion-based framework is proposed to model the distribution of BTM PV generation conditioned on net consumption and exogenous variables. The methodology provides calibrated and sharp probabilistic estimates and is further extended to jointly disaggregate multiple DERs, such as heat pumps. Validation on real-world datasets demonstrates reliable uncertainty quantification and strong generalization under limited training data.
  

DOCTORAL DEGREE IN ENVIRONMENTAL ENGINEERING

• CORREA GONZÁLEZ, SANDRA: Anaerobic digestion-based biorefineries to advance circularity in the olive oil sector
  
  Author: CORREA GONZÁLEZ, SANDRA
  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: 27/02/2026
  Deposit END date: 12/03/2026
  Thesis director: FERRER MARTI, IVET | PASSOS LOPES, FABIANA
  Thesis abstract: Every year, the olive oil industry generates large quantities of olive pomace, a by-product that currently represents an environmental problem but has great potential for the recovery of bio-based products and bioenergy within a biorefinery framework.In this context, the objective of this PhD thesis was to develop and evaluate anaerobic digestion–based biorefinery strategies for the valorisation of olive pomace. Specifically, the limitations of olive pomace mono-digestion were assessed, and co-digestion was investigated as a strategy to improve process stability and methane production. Biorefinery models for the recovery of bio-based products and biogas were examined, the agronomic potential of the resulting digestates was analysed, and last anaerobic fermentation was explored as an alternative valorisation pathway.The results demonstrated that olive pomace mono-digestion is unstable due to substrate-related properties, including acidic pH, lack of alkalinity and nutrients, high C/N ratio, and the presence of phenolic compounds. These limitations led to the accumulation of volatile fatty acids, inhibition of methanogenesis, and collapse of the microbial community, resulting in very low methane yields.Co-digestion with nitrogen-rich co-substrates, specifically from the swine farming sector, proved to be an effective strategy to overcome these limitations. In particular, pig slurry provided alkalinity, nutrients, and water, enabling stable operation, increased microbial diversity, and enhanced methane yields (145 mL CH₄·g⁻¹ VS). In contrast, co-digestion with pig manure achieved higher methane yields (289 mL CH₄·g⁻¹ VS) but exhibited lower resilience under stress conditions.Two anaerobic digestion–based biorefinery models were developed in this thesis. In the first, an ionic liquid pre-treatment ([Et₃NH][HSO₄], 120 °C, 1 h) was applied to recover lignin nanoparticles from olive pomace, followed by anaerobic digestion of the residual fraction. Analysis of the residual olive pomace revealed a less compact surface structure and greater bioaccessibility for microorganisms, resulting in higher methane yields during co-digestion with pig slurry compared to untreated olive pomace (173 mL CH₄·g⁻¹ VS). In the second biorefinery model, a thermal process (water, 100 °C, 45 min) was used to recover natural dyes for textile applications. The residual olive pomace fraction retained 88% of the initial methane potential. During semi-continuous reactor operation, co-digestion of this fraction with pig slurry achieved a methane yield of 157 mL CH₄·g⁻¹ VS.Digestates derived from the co-digestion of untreated and residual olive pomace with pig slurry exhibited favourable agronomic properties, such as near-neutral pH, balanced nutrient content, and partially stabilised organic matter. However, phytotoxicity at high application rates and elevated Hg concentrations highlight the limitations of these digestates and the need to blend them with other organic fertilisers to ensure safe soil application.Anaerobic fermentation was identified as an alternative or complementary pathway for olive pomace valorisation. Temperature and hydraulic retention time strongly influenced volatile fatty acid yields and profiles, as well as microbial community composition.Overall, this thesis demonstrates the potential of anaerobic digestion–based biorefineries for the valorisation of olive pomace and the production of value-added bio-based products, renewable energy, and organic fertilisers, contributing to the development of circular and resilient agro-industrial systems.
  

DOCTORAL DEGREE IN MECHANICAL, FLUIDS AND AEROSPACE ENGINEERING

• KHOSRAVI, HAMID: Enhancing microfluidic and electrochemical sensors for biological and environmental analysis
  
  Author: KHOSRAVI, HAMID
  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: 27/02/2026
  Deposit END date: 12/03/2026
  Thesis director: CASALS TERRE, JASMINA
  Thesis abstract: The transition toward low-cost, portable, and environmentally conscious analytical technologies has intensified the pursuit of sustainable alternatives to conventional laboratory instrumentation. This thesis develops paper-based and electrochemical sensing platforms that prioritize circular-economy principles by employing renewable and waste-derived materials. Non-wood cellulose fibers were selected as substrates for microfluidic paper-based analytical devices (μPADs), while industrial mill scale was valorized to synthesize magnetite nanoparticles for electrode modification, demonstrating that sustainability and high analytical performance can be synergistic.In the first study, μPADs fabricated from alternative cellulose sources were evaluated. Their fiber morphology and porosity strongly influenced capillary flow and colorimetric responses. Compared to commercial cellulose papers, non-wood substrates enabled substantially faster wicking and significantly reduced detection time, underscoring their suitability for rapid, low-resource diagnostics.The second study focused on lactate detection using magnetite-modified electrodes. Wastederived Fe₃O₄ nanoparticles enhanced electron transfer and enzyme immobilization, enabling an exceptionally broad detection range alongside high sensitivity and a low detection limit. To the best of our knowledge, this work represents the first demonstration of a lactate biosensing platform that simultaneously achieves such a wide dynamic range while retaining high analytical sensitivity, making it suitable for applications from trace physiological monitoring to highly concentrated food and fermentation environments.Finally, a novel electrochemical strategy was developed for polyethylene terephthalate (PET) microplastic quantification in water. Leveraging the natural affinity between PET and magnetite nanoparticles, the approach transitions from the traditional use of magnetite for magnetic pre-concentration toward direct and quantitative electrochemical measurement, successfully validated in synthetic and real water matrices.Overall, this thesis demonstrates that renewable and waste-derived materials from non-wood cellulose to mill-scale-derived magnetite can serve as functional components in advanced sensing platforms, advancing sustainable analytical technologies for biomedical and environmental applications.
  
• SABÁN FOSCH, ALEJANDRO: End-to-end design and development of an Autonomous Flight Safety System enabling reusable space missions in Europe
  
  Author: SABÁN FOSCH, ALEJANDRO
  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: 05/03/2026
  Deposit END date: 18/03/2026
  Thesis director: SORIA GUERRERO, MANUEL | DIEZ LLEDO, EDUARD | SUREDA ANFRES, MIQUEL
  Thesis abstract: This dissertation presents the design, development, and validation of an Autonomous Flight Safety System (AFSS) tailored to the operational and regulatory needs of reusable launch vehicles in Europe. Motivated by the shift from expendable rockets to reusable systems and the consequent need for autonomous range safety, the research situates itself at the intersection of technology, safety assurance, and certification.A requirement-driven approach, grounded in European and international regulations, ensured alignment with certification pathways. A review of current FSS and regulatory frameworks established the baseline from which requirements were derived. These were structured through a model-based systems engineering methodology, implemented in ARCADIA and SysML, guiding functional decomposition and definition of a three-layered architecture. The AFSS design comprises four application modules: navigation, flight dynamics assessment, decision-making, and Integrated Vehicle Health Management (IVHM).Each module was independently implemented and validated. The navigation subsystem met outage-handling requirements, reliably bridging data gaps. The flight dynamics assessment integrated 3D flight corridor checks, and impact prediction with aerodynamic effects and dispersion evaluation at low operational cost. For reusable launchers, the IVHM subsystem is essential, as safe operation requires monitoring systems for re-entry. This module classified anomalies accurately, highlighting the trade-off between expert-tuned and data-driven approaches due to sensitivity to membership function parametrisation. The decision-making logic consistently executed termination rules under nominal and degraded conditions, confirming robustness.A RAMS (Reliability, Availability, Maintainability, and Safety) analysis critically assessed maturity. Navigation and decision-making were identified as the most safety-critical functions, with redundancy mitigating risks but leaving common-mode vulnerabilities. Prototype hardware (HW) was selected according to Technology Readiness Level (TRL) criteria, suitable for ground validation at TRL 7 system level. This reflected a focus on validating software and architecture, while dedicated space-qualified HW -required for certification under harsher conditions such as radiation and vibration- lay beyond scope.Integration testing guaranteed the correctness of the AFSS prototype before the ground campaign at the Kiruna spaceport to achieve TRL 7, a milestone in European AFSS development. The prototype demonstrated coherent behaviour across processors, reliable synchronization between redundant chains, and real-time telemetry from the Real-time target machine. Although processing loads neared the limits of the selected low-end HW, it met its main objective: validating the complete AFSS software chain. Nonetheless, borderline safety decisions under certain conditions showed that resilience depends on algorithmic choices, parametrisation, and execution margins.The research shows AFSS architectures are technically feasible, regulation-aware, and progressing towards operational use, though challenges remain. Future work should address processor scalability with multi-core, space-qualified platforms; enhance navigation robustness against GNSS jamming and spoofing; extend IVHM towards prognostics; and evaluate navigation architectures (IMU-only versus integrated IMU/GNSS) once launcher avionics are defined. Equally critical is institutional progress: certifying AFSS will require new regulatory frameworks and joint experimental programmes aligning technical validation with policy evolution.By combining regulatory awareness, rigorous engineering, and validation to TRL 7, this dissertation contributes not only a prototype but also a roadmap. It demonstrates feasibility while clarifying remaining challenges, providing a foundation for the safe deployment of autonomous flight safety in Europe's reusable launchers.
  

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
  Deposit END date: 25/03/2026
  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 PHOTONICS

• PUJOL CLOSA, MARIA DEL PILAR: Wave Propagation in Hyperbolic Metamaterial Waveguides
  
  Author: PUJOL CLOSA, MARIA 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 PHOTONICS
  Department: Institute of Photonic Sciences (ICFO)
  Mode: Normal
  Deposit date: 11/03/2026
  Deposit END date: 24/03/2026
  Thesis director: ARTIGAS GARCIA, DAVID
  Thesis abstract: Low-loss waveguides are essential for energy-efficient photonic circuits, optical communications, and sensing applications. Over the past century, two lossless phenomena—Dyakonov modes and Bound States in the Continuum (BICs)—have been discovered in anisotropic waveguides, where permittivities differ but share the same sign. Hyperbolic metamaterials (HMMs) exhibit extreme anisotropy, with ordinary and extraordinary permittivities of opposite signs, enabling unconventional light manipulation. Their unique properties have attracted broad interest for applications including subdiffraction imaging, spontaneous emission control, and enhanced light-matter interactions. This raises a fundamental question: can extreme hyperbolic anisotropy support novel confinement mechanisms or new regimes of lossless propagation? Prior research on HMM waveguides has been constrained to simplified models or propagation along principal axes, leaving systematic exploration of arbitrary propagation directions, and the phenomena they may reveal, as a critical gap.To address this gap, this thesis develops a semi-analytical computational framework that combines a transfer-matrix formulation with a complex-plane Newton-Raphson root finder, enabling stable tracking of guided and leaky modes for arbitrary propagation directions. This tool allows systematic exploration of a wide range of parameters and configurations previously difficult to study.This thesis provides the most comprehensive investigation to date of light propagation in planar HMM waveguides. For the first time, the work analyzes both type I and type II HMM waveguides across all in-plane propagation directions and with arbitrary optic axis orientations. The analysis reveals how hyperbolic anisotropy fundamentally influences polarization, confinement, polarization exchange between modes, mode ordering, radiation mechanisms, and slow light arising from topological transitions. This establishes general trends, identifies new guiding regimes, and maps the landscape of wave phenomena in these extreme anisotropic systems.The exploration of leaky modes enabled a key discovery: Dirac points embedded in the Continuum (DECs), a novel class of topological degeneracy in non-Hermitian systems. DECs emerge when a symmetry-protected BIC and an interferometric BIC intersect linearly. At this intersection, the system exhibits a real eigenvalue, two orthogonal modes, and zero radiation loss—locally Hermitian behavior despite being embedded in a non-Hermitian system. The presence of both BICs suppresses Exceptional Points (EPs) and collapses the Fermi arc to a single point. Because DECs arise from universal BIC interactions rather than material-specific properties, this phenomenon extends beyond hyperbolic media, with implications in the fields of topological photonics and non-Hermitian physics.This thesis demonstrates the framework’s generality and reliability through application to anisotropic liquid-crystal waveguides, where predicted BIC trajectories match experimental observations, and to $\sigma$-near-zero metasurfaces, where the framework accurately reproduces published dispersion diagrams. These validations confirm its applicability beyond hyperbolic systems.This thesis establishes a comprehensive theoretical and computational understanding of wave propagation in planar HMM waveguides for both type I and type II configurations and discovers DECs as a novel physical phenomenon with implications beyond hyperbolic media. By revealing how extreme anisotropy enables new guiding regimes and loss suppression, this work advances the understanding of light confinement in open, strongly anisotropic systems and provides new routes for designing low-loss photonic devices.
  

DOCTORAL DEGREE IN SIGNAL THEORY AND COMMUNICATIONS

• GORT JELMER DIRK, BEREND: AI-Driven Zero-Touch Orchestration of Edge-Cloud Services
  
  Author: GORT JELMER DIRK, BEREND
  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: 10/03/2026
  Deposit END date: 23/03/2026
  Thesis director: ANTONOPOULOS, ANGELOS | UMBERT JULIANA, ANNA
  Thesis abstract: 6G networks demand orchestration systems capable of managing thousands of distributed microservices under sub-millisecond latency constraints. Traditional centralized approaches introduce unacceptable delays, create single points of failure in heterogeneous edge-cloud infrastructures, and require constant attention from human operators. This dissertation addresses three critical challenges: (1) computational constraints that prevent the deployment of predictive models on edge devices, (2) lack of generalization of models across diverse types of applications, and (3) lack of validated autonomous orchestration without human intervention.To address these challenges, this dissertation develops three complementary frameworks that combine lightweight machine learning, attention-based deep learning, and agentic artificial intelligence for zero-touch service management in distributed 6G edge-cloud environments.The first contribution, AERO (Adaptive Edge-cloud Resource Orchestration), addresses the challenge of running predictions on resource-constrained edge devices. Current transformer models require millions of parameters (e.g., Pathformer: 2.4M), making them impractical for edge deployment. AERO achieves competitive accuracy with only 599 parameters, making edge deployment feasible and reducing reliance on cloud round-trips when local inference is preferred. Evaluations demonstrate sub-millisecond inference (0.38ms), 13% energy savings, and 99% fewer SLA violations compared to reactive scheduling, which allocates resources only after demand changes occur.The second contribution, OmniFORE (Framework for Optimization of Resource Forecasts in Edge-cloud networks), addresses the operational challenge of maintaining separate models per application. A single OmniFORE model generalizes across heterogeneous workloads without retraining, replacing the need for dedicated per-application models. Cross-dataset evaluation on industry-standard benchmarks (Google and Alibaba production traces) demonstrates 30% better accuracy than ModernTCN while maintaining 15× faster inference than AGCRN.The third contribution, AgentEdge, addresses the challenge of agentic orchestration in distributed edge-cloud environments. Existing agent frameworks target generic domains or centralized cloud infrastructures, leaving distributed 6G environments without autonomous management solutions. AgentEdge introduces multi-agent orchestration to this domain, translating natural language intent (e.g., "deploy with low latency") into validated orchestration actions across heterogeneous infrastructure. Evaluations demonstrate 78.3% success rate (2.76× higher than single-agent baselines), 10× reduction in API call variability, and power savings up to 300.8W across deployments scaling from 8 to 35 nodes.The research has produced 5 journal publications, 3 international conference papers in IEEE venues, and 1 Elsevier book chapter.
  

DOCTORAL DEGREE IN STATISTICS AND OPERATIONS RESEARCH

• ROGNON, PAUL JORIS DENIS: Improving variable selection properties by leveraging external data.
  
  Author: ROGNON, PAUL JORIS DENIS
  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 STATISTICS AND OPERATIONS RESEARCH
  Department: Department of Statistics and Operations Research (EIO)
  Mode: Normal
  Deposit date: 09/03/2026
  Deposit END date: 20/03/2026
  Thesis director: ROSSELL RIBERA, DAVID | ZWIERNIK, PÌOTR
  Thesis abstract: Sparse high-dimensional signal recovery is only possible under certain conditions on the number of parameters, sample size, signal strength, and underlying sparsity. I show that leveraging external information, as possible with data integration or transfer learning, allows pushing these mathematical limits. Specifically, I consider external information-dependent l0 penalties and Bayesian variable selection methods, show that they attain model selection consistency under milder conditions than standard methods, and that they also attain faster model recovery rates. First, I obtain results for oracle-based penalties and prior inclusion probabilities that have access to perfect sparsity and signal strength information. Those results provide an understanding of how and when external information helps variable selection. They also provide a theoretical benchmark to evaluate practical non-oracle selection methods using external information. Subsequently, I propose data-based procedures grounded in empirical Bayes methods that leverage external information to ease variable selection and do not require an oracle. I derive their properties in the particular case where external information partitions the set of variables in blocks with potentially distinct characteristics. Finally, I discuss a computational framework for the incorporation of external information in Bayesian variable selection through empirical Bayes in the general case.
  

DOCTORAL DEGREE IN STRUCTURAL ANALYSIS

• BORGES CAVALCANTI, DANILO: Finite Element Method with Embedded Strong Discontinuities for Coupled Hydro-Mechanical Problems
  
  Author: BORGES CAVALCANTI, DANILO
  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: Change of supervisor + Article-based thesis
  Deposit date: 09/03/2026
  Deposit END date: 20/03/2026
  Thesis director: DE POUPLANA SARDÀ, IGNASI | CAMPOS RAMOS MARTHA, LUIZ FERNANDO | DE MESQUITA ROEHL, DEANE
  Thesis abstract: Coupled hydro–mechanical (HM) processes in fractured porous media govern the performance and safety of several subsurface engineering applications, where pressure-driven changes in stress and permeability can control injectivity, leakage pathways, and fault reactivation potential. This thesis develops a robust and versatile finite element formulation for transient HM problems in the presence of pre-existing strong discontinuities that remains practical for integration into standard finite element workflows. The proposed approach is formulated within the Embedded Finite Element Method (E-FEM) and grounded on the Strong Discontinuity Approach (SDA), enabling an implicit representation of fractures and faults while circumventing mesh conformity constraints. A unified description is introduced to model discontinuities acting either as preferential flow paths or as hydraulic barriers, capturing the longitudinal flow along the discontinuity and the transversal exchange with the porous matrix in steady-state and transient settings. The formulation is systematically verified against discrete fracture models with interface elements and applied to benchmark problems representative of fractured-reservoir conditions, including a coupled fault reactivation scenario. In addition, the thesis investigates the occurrence of spurious oscillations in cohesive traction fields along embedded discontinuities and demonstrates that the choice of an SDA-based embedded formulation can markedly improve traction smoothness. These improvements strengthen the use of E-FEM for HM assessments involving pre-existing fractures and faults.