Public display of deposited theses

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 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 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 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.