Doctoral School

Reading date: 26/06/2026

• CHEN, YAOGANG: Multi-Temporal Polarimetric InSAR Deformation Monitoring Considering Spatiotemporal Scattering Variability
  
  Author: CHEN, YAOGANG
  Programme: DOCTORAL DEGREE IN SIGNAL THEORY AND COMMUNICATIONS
  Department: Department of Signal Theory and Communications (TSC)
  Mode: Change of supervisor + Article-based thesis
  Deposit date: 28/05/2026
  Reading date: 26/06/2026
  Reading time: 08:00
  Reading place: Room 214 of the Geosciences Building,
  Thesis director: MALLORQUI FRANQUET, JORDI JOAN | HU, JUN
  Thesis abstract: Interferometric Synthetic Aperture Radar (InSAR) enables millimeter-level measurements of surface deformation over large areas and long time spans, and has become an important tool for geohazard monitoring and infrastructure safety assessment. However, in natural environments with dense vegetation, intensive agricultural activity, or strong surface disturbance, rapid variations in scattering mechanisms often cause severe coherence loss, which significantly limits the accuracy and density of deformation monitoring. By introducing multi-polarization observations, multi-temporal Polarimetric InSAR (MT-PolInSAR) improves InSAR performance in complex low-coherence scenarios. Nevertheless, existing MT-PolInSAR methods still face two major limitations: insufficient consideration of the spatial and temporal variability of scattering mechanisms, and inadequate exploitation of the complementary information in the polarimetric, temporal, and spatial domains within a unified framework.To address these issues, this thesis investigates phase optimization and deformation monitoring for MT-PolInSAR under low-coherence conditions. A systematic methodology is developed by exploiting the redundancy and scattering information contained in polarimetric SAR data, including homogeneous filtering for small datasets, polarimetric phase optimization with spatially varying scattering mechanisms, joint phase optimization in the temporal and polarimetric domains, and sequential near-real-time processing.First, a homogeneous filtering method for MT-PolInSAR small datasets is proposed. By introducing spatial covariance structures and jointly exploiting temporal and polarimetric redundancy, the method improves pixel discrimination and enhances the signal-to-noise ratio. Experiments on simulated data and Barcelona Airport data demonstrate improved phase quality, more stable coherence estimation, and better preservation of spatial structures.Second, an improved polarimetric phase optimization method, termed ImESPO, is proposed to account for spatial variations in scattering mechanisms. Unlike conventional methods, it explicitly considers local scattering heterogeneity during polarimetric projection. Results show that ImESPO achieves more stable coherence gains and phase consistency in heterogeneous areas, improving phase estimation accuracy by more than 20%.Third, a joint phase optimization model combining the temporal and polarimetric dimensions, termed JPTPO, is developed. By jointly modeling both dimensions within a unified statistical framework, the method achieves improved phase consistency and more stable deformation inversion results on both simulated and real datasets.Finally, a near-real-time MT-PolInSAR deformation monitoring method is proposed for rapid-decorrelation scenarios. Applied to landslide monitoring in the Fengjie area of the Three Gorges Reservoir, the proposed method increases measurement density by a factor of four and improves monitoring accuracy from 18.4% to 71.8%, while maintaining near-real-time capability.Overall, this thesis advances the theory and methodology of MT-PolInSAR deformation monitoring in complex low-coherence environments, providing new solutions for high-precision and continuous monitoring of landslides and other geohazards.
  

Reading date: 29/06/2026

• LOPEZ ALVAREZ, CIBRAN: Unveiling correlated charge dynamics and recombination pathways in energy materials via quantum simulations and machine learning
  
  Author: LOPEZ ALVAREZ, CIBRAN
  Programme: DOCTORAL DEGREE IN COMPUTATIONAL AND APPLIED PHYSICS
  Department: Department of Physics (FIS)
  Mode: Article-based thesis
  Deposit date: 19/05/2026
  Reading date: 29/06/2026
  Reading time: 10:00
  Reading place: Sala Polivalent de l'edifici A del Campus Diagonal-Besòs
  Thesis director: CAZORLA SILVA, CLAUDIO | SAUCEDO SILVA, EDGARDO ADEMAR
  Thesis abstract: Understanding how atomic-scale mechanisms govern ionic and electronic transport is crucial for the design of next-generation energy materials. Here, we combine first-principles simulations and machine-learning techniques to provide predictive and transferable approaches for modelling and understanding at the atomistic scale solid-state electrolytes and pnictogen chalcohalide (MChX, with M = Bi, Sb; Ch = S, Se; and X = I, Br) photovoltaics.Our first-principles calculations and unsupervised learning investigations revealed that ionic diffusion in solid-state electrolytes is fundamentally governed by correlated motion of multiple ions. These cooperative events are strongly influenced by lattice vibrations, linking ionic conductivity to both vibrational dynamics and elastic properties of the non-diffusive crystal framework. Characteristic correlation lengths, remarkably independent of temperature, were identified, providing new descriptors for the design of fast-ion conductors. In addition, a comprehensive first-principles simulations database and automated analysis tools were developed, offering a scalable platform for understanding ionic transport across diverse material families and compositions.At the same time, first-principles simulations combined with deep learning and device-level modeling identified and experimentally validated MChX-based solid solutions with tunable band-gaps (1.2–2.1 eV) and strong absorption coefficients (up to 66 μm⁻¹), demonstrating the potential of MChX tandems to achieve short-circuit currents exceeding 18 mA/cm². Further ab initio calculations revealed chalcogen vacancies as dominant non-radiative centers in MChX, potentially limiting efficiencies down to 24% in BiSeI. However, targeted anion substitution and synthesis conditions were shown to suppress these detrimental recombination-active centers.Together, the work realised during this doctorate establishes generalizable frameworks that connect atomistic mechanisms to macroscopic device performance. The methodologies introduced here are readily transferable to other families of materials and functional applications, providing a roadmap for the rational design of high-performance, sustainable energy technologies.
  

• SRIVASTAVA, ANUBHAV KUMAR: Quantum simulations of spin systems for optimal quantum metrology
  
  Author: SRIVASTAVA, ANUBHAV KUMAR
  Programme: DOCTORAL DEGREE IN PHOTONICS
  Department: Institute of Photonic Sciences (ICFO)
  Mode: Normal
  Deposit date: 01/06/2026
  Reading date: 29/06/2026
  Reading time: 10:30
  Reading place: Mir-Puig Elements
  Thesis director: LEWENSTEIN, MACIEJ | PLODZIEN, MARCIN
  Thesis abstract: Quantum mechanics both constrains and empowers precision measurement: the uncertainty principle imposes fundamental limits on parameter estimation, which quantum resources such as entanglement and superposition can saturate. Quantum metrology develops protocols that exploit non-classical probe states and optimal data processing to surpass the standard quantum limit. In practice, however, realizing this advantage requires solving three problems: designing experimentally feasible many-body probes with near-optimal sensitivity, certifying metrological resources from incomplete measurement data, and implementing optimal readout schemes that remain tractable at scale. This thesis develops a unified framework for all three challenges, combining quantum simulation, convex optimization, and classical-shadow techniques to bring quantum-enhanced metrology closer to experimental reality.The first part addresses quantum thermometry at nano- and sub-nanokelvin scales. Using the quantum Fisher information (QFI) as the sensitivity measure, we show that an experimentally accessible system of spinless fermions in a one-dimensional optical lattice, described by the Rice–Mele (RM) model, realizes a near-optimal local quantum thermometer approaching the fundamental Cramér–Rao bound. We characterize how the topological and trivial regimes, the lattice filling, and a tunable staggered potential control its sensitivity, and show that the probe equilibrates with a coupled bath without perturbing it. We further analyse a global thermometry scheme based on classical-shadow tomography of thermal states, comparing its sample complexity with standard protocols.The second part develops two complementary tools for quantum-enhanced sensing and its certification. We introduce a sensor based on a frustrated Kitaev trimer whose nonlinear spectral response implements a thresholded rectifying detector: for a zero-mean omnidirectional signal, the accumulated phase vanishes below a tunable threshold and, above it, is proportional to the signal's second moment. Entangled multi-trimer configurations attain Heisenberg-limited sensitivity. We then formulate a semidefinite programme (SDP) that computes the minimal QFI compatible with incomplete expectation-value data, yielding rigorous lower bounds without full state tomography. Applied to multi-headed cat states generated by one-axis-twisting dynamics, the SDP certifies metrological usefulness from low-order moments more tightly than conventional squeezing inequalities.The third part addresses the gap between optimal measurement schemes and the measurements achievable on current quantum platforms. The optimal observable saturating the quantum Cramér–Rao bound is generically a highly nonlocal operator whose Pauli weight grows with system size. We introduce Clifford lensing, a framework in which classically simulable Clifford circuits map the optimal observable onto an operator of reduced Pauli weight, refocusing distributed phase information onto fewer qubits. We establish a correspondence between quantum error-correcting codes and interferometric constructions enforcing deterministic phase kickback, and develop metrologically sufficient partial-shadow tomography protocols that preserve the full QFI. The resulting schemes require exponentially fewer samples than naïve shadow estimation and are validated on liquid-state nuclear magnetic resonance (NMR) systems of up to 15 qubits.Together, these results demonstrate that near-optimal quantum metrology is achievable with accessible probes, data-efficient certification, and scalable readout, providing a unified route from fundamental metrological bounds to practical quantum-enhanced sensing.
  

Reading date: 30/06/2026

• CUMELLES CÉSPEDES, JOEL: System Identification of High-Performance Paraglider-Harness/Pilot Dynamics: From Modelling to Flight Test Data
  
  Author: CUMELLES CÉSPEDES, JOEL
  Programme: DOCTORAL DEGREE IN AEROSPACE SCIENCE AND TECHNOLOGY
  Department: Department of Physics (FIS)
  Mode: Normal
  Deposit date: 02/06/2026
  Reading date: pending
  Reading time: pending
  Reading place: pending
  Thesis director: CASAS PIEDRAFITA, JAIME OSCAR | ORTEGA AGODINO, ENRIQUE
  Thesis abstract: Despite substantial advances in the design, development, and analysis of high-performance paraglider–harness/pilot systems, evaluation methods remain predominantly qualitative and rely heavily on trial-and-error testing. Quantitative in-flight experimental data for these configurations remain scarce, and traditional parafoil–payload models frequently fail to capture the complex geometry, aerodynamic interactions, and dynamic behaviour characteristic of modern high-performance paragliders.This thesis proposes an integrated framework that combines a high-fidelity dynamic model specifically adapted to these systems with a low-cost instrumental platform for model validation using experimental flight data. A nonlinear eight-degrees-of-freedom dynamic model of the high-performance paraglider–harness/pilot system is derived, explicitly incorporating apparent mass effects. Aerodynamic forces and moments are obtained by coupling the model to a computationally efficient horseshoe vortex method solver based on Prandtl’s lifting-line theory and augmented with viscous drag corrections derived from airfoil section polars. This approach enables the direct computation of aerodynamic loads from the actual canopy architecture. Additional refinements are incorporated to improve model fidelity, including a parametric aerodynamic model of the harness/pilot assembly, a generalised aerodynamic drag formulation for the suspension lines, and a distributed representation of brake input along the span and chord.A modular, low-cost, wireless instrumentation platform was developed to support model validation and generate quantitative datasets. This platform integrates distributed accelerometers, gyroscopes, magnetometers, as well as barometric pressure, temperature, and humidity sensors; a GPS module; and a multi-hole probe installed on one of the paraglider suspension lines. The platform was implemented on a commercial high-performance paraglider and evaluated through two dedicated flight-test campaigns.Finally, a system identification approach based on output error methods is implemented to adjust model parameters, enabling comparison of flight data and model predictions across manoeuvres. The results exhibit strong agreement in dynamic response and aerodynamic behaviour, confirming the suitability of the proposed modelling and instrumentation framework. This framework will support future studies of high-performance paraglider–harnesses/pilot systems with quantitative data, thereby enhancing understanding and further development.
  

• ROCA NONELL, ALEIX: On the interaction between the Linux kernel and runtime systems
  
  Author: ROCA NONELL, ALEIX
  Programme: DOCTORAL DEGREE IN COMPUTER ARCHITECTURE
  Department: Department of Computer Architecture (DAC)
  Mode: Normal
  Deposit date: 27/05/2026
  Reading date: 30/06/2026
  Reading time: 11:00
  Reading place: Sala C6-E106
  Thesis director: AYGUADÉ PARRA, EDUARD | BELTRAN QUEROL, VICENÇ
  Thesis abstract: High-Performance Computing (HPC) underpins scientific discovery and industrial innovation, yet its progress is tightly coupled to how effectively applications exploit modern supercomputers, which consist of thousands of nodes and hundreds of cores. Most supercomputing centers rely on the Linux Kernel at the core of their Operating System (OS). Linux provides applications with the foundation to interface with the capabilities of the underlying hardware, and while it excels in handling sequential and lightly parallel workloads, it might not suffice for highly parallel demands. To bridge the gap between application needs and the OS policy, developers increasingly rely on programming models to express parallelism through fork-join or data-flow dependencies that delegate work distribution to runtime systems. Runtimes simplify parallel programming by hiding Linux complexity and providing user-friendly alternatives that allow developers to focus on their algorithms instead of the underlying OS and hardware details. The coupling between Linux and runtime systems is essential to efficiently map the application's parallel workload to system resources with minimum OS interference and to maximize spatial and temporal locality. This is particularly important as the convergence of HPC, data analytics, and AI is transforming traditional HPC workloads that typically consisted of a single parallelized application into an ecosystem of services that interact with each other, opening new cooperation challenges.This thesis presents a broad exploration of opportunities to tighten the bond between Linux and runtime systems from the I/O, memory, scheduling, and tracing subsystems. We analyze bottlenecks found in real scenarios and design and implement software solutions at both kernel and user level with the aim of improving overall performance, throughput, and latency.On the I/O subsystem, we explore the challenge of integrating blocking operations in runtime systems. Blocking operations are oblivious from the runtime core management perspective and directly affect their capacity to keep cores busy with useful work. Better, seamless detection of such operations has the potential to enable I/O and computation overlapping.On the memory subsystem, we face the challenge of transitioning from traditional homogeneous to heterogeneous memory systems. Traditional systems and applications are designed for homogeneous memory; however, a transparent kernel- or runtime-level solution could enable these applications to exploit the new paradigm without modification.On the process scheduling subsystem, we extensively review the impact of context-switching for fine-grained suspendable tasks. Fine-grained parallel applications are characterized by exposing huge amounts of parallelism while consisting of small computational bursts. These pose a burden to the runtime, which needs to react quickly to keep cores busy with short-lived workloads, and to the kernel, which may need to swap threads on cores. In these cases, the context-switching latency becomes a bottleneck, and improving it can considerably raise performance.Additionally, we analyze the impact of oversubscription on multi-process and multi-runtime workloads. Although these scenarios are usually avoided, the new paradigm powered by the AI adoption makes them increasingly more common. By rethinking the current thread scheduling policies, we can transparently improve performance while maintaining compatibility.Lastly, On the tracing subsystem, we explore the availability of profiling tools for both runtime and kernel and elaborate on the benefits of having a conjoined solution to better understand their interactions, such as the detection of runtime imbalances caused by OS noise.