Assisted generation of complex computational kernels in solid mechanics
The behavior laws used in numerical simulations describe the physical characteristics of simulated materials. As our understanding of these materials evolves, the complexity of these laws increases. Integrating these laws is a critical step for the performance and robustness of scientific computations. Therefore, this step can lead to intrusive and complex developments in the code.
Many digital platforms, such as FEniCS, FireDrake, FreeFEM, and Comsol, offer Just-In-Time (JIT) code generation techniques to handle various physics. This JIT approach significantly reduces the time required to implement new simulations, providing great versatility to the user. Additionally, it allows for optimization specific to the cases being treated and facilitates porting to various architectures (CPU or GPU). Finally, this approach hides implementation details; any changes in these details are invisible to the user and absorbed by the code generation layer.
However, these techniques are generally limited to the assembly steps of the linear systems to be solved and do not include the crucial step of integrating behavior laws.
Inspired by the successful experience of the open-source project mgis.fenics [1], this thesis aims to develop a Just-In-Time code generation solution dedicated to the next-generation structural mechanics code Manta [2], developed by CEA. The objective is to enable strong coupling with behavior laws generated by MFront [3], thereby improving the flexibility, performance, and robustness of numerical simulations.
The selected PhD candidate should have a solid background in computational science and a strong interest in numerical simulation and C++ programming. They should be capable of working independently and demonstrate initiative. The doctoral student will benefit from guidance from the developers of MFront and Manta (CEA), as well as the developers of the A-Set code (a collaboration between Mines-Paris Tech, Onera, and Safran). This collaboration within a multidisciplinary team will provide a stimulating and enriching environment for the candidate.
Furthermore, the thesis work will be enhanced by the opportunity to participate in conferences and publish articles in peer-reviewed scientific journals, offering national and international visibility to the thesis results.
The PhD will take place at CEA Cadarache, in south-eastern France, in the Nuclear Fuel Studies Department of the Institute for Research on Nuclear Systems for Low-Carbon Energy Production (IRESNE)[4]. The host laboratory is the LMPC, whose role is to contribute to the development of the physical components of the PLEIADES digital platform [5], co-developed by CEA and EDF.
[1] https://thelfer.github.io/mgis/web/mgis_fenics.html
[2] MANTA : un code HPC généraliste pour la simulation de problèmes complexes en mécanique. https://hal.science/hal-03688160
[3] https://thelfer.github.io/tfel/web/index.html
[4] https://www.cea.fr/energies/iresne/Pages/Accueil.aspx
[5] PLEIADES: A numerical framework dedicated to the multiphysics and multiscale nuclear fuel behavior simulation https://www.sciencedirect.com/science/article/pii/S0306454924002408
Multiphysic modeling of sintering of nuclear fuel pellet: effect of atmosphere on shrinkage kinetics
Uranium dioxide (UO2) fuels used in nuclear power plants are ceramics, for which solid-phase sintering is a key manufacturing step. The sintering stage involves heat treatment under controlled partial O2 pressure that induces coarsening of UO2 grain and then consolidation and densification of the material. Grain growth induce material densification and macroscopic shrinkage of the pellet. If the green pellet (powder obtained by pressing, manufacturing step before sintering) admit a highly heterogeneous density, this gradient leading to differential shrinkage and the appearance of defects. Furthermore, the sintering atmosphere, i.e., the gas composition in the furnace, impacts grain growth kinetics and thus the shrinkage of the pellet. Advanced simulation is the key to improving understanding of the mechanisms observed as well as optimizing manufacturing cycles.
The PhD thesis aims at developing a Thermo-chemo-mechanical modeling of sintering to simulate the impact of the gas composition and properties on the pellet densification. This scale will enable us to take into account not only the density gradients resulting from pressing, but also the oxygen diffusion kinetics that have a local impact on the densification rate, which in turn impacts the transport process. Therefore, a multiphysics coupling phenomenon has to be modelled and simulated.
This thesis will be conducted within the MISTRAL joint laboratory (Aix-Marseille Université/CNRS/Centrale Marseille CEA-Cadarache IRESNE institute). The PhD student will leverage his results through publications and participation in conferences and will have gained strong skills and expertise in a wide range of academic and industrial sectors.
Understanding the signals emitted by moving liquids
Elasticity is one of the oldest physical properties of condensed matter. It is expressed by a constant of proportionality G between the applied stress (s) and the deformation (?): s = G.? (Hooke's law). The absence of resistance to shear deformation (G' = 0) indicates liquid-like behavior (Maxwell model). Long considered specific to solids, shear elasticity has recently been identified in liquids at the submillimeter scale [1].
The identification of liquid shear elasticity (non-zero G') is a promise of discoveries of new liquid properties. For example, do we know that a confined liquid changes temperature under flow? Yet no classical model (Poiseuille, Navier-Stokes, Maxwell) predicts the effect because without long-range correlation between molecules (i.e. without elasticity), the flow is dissipative, therefore athermal. For a change in temperature to be flow induced (without a heat source), the liquid must have elasticity and this elasticity must be stressed [1,2].
The PhD thesis will explore how the mechanical energy of the flow is converted in a thermal response [2]. We will exploit the capacity of conversion to develop a new generation of microfluidic devices (patent FR2206312).
We will also explore the impact of the wetting on the liquid flow, and reciprocally, we will examine how the liquid flow modifies the solid dynamics (THz) of the substrate [3]. Powerful methods only available in Very Large Research Facilities such as the ILL will be used to probe the non-equilibrium state of solid phonons. Finally, we will strengthen our existing collaborations with theoreticians.
The PhD topic is related to wetting, macroscopic thermal effects, phonon dynamics and liquid transport.
1. A. Zaccone, K. Trachenko, “Explaining the low-frequency shear elasticity of confined liquids" PNAS, 117 (2020) 19653–19655. Doi:10.1073/pnas.2010787117
2. E. Kume, P. Baroni, L. Noirez, “Strain-induced violation of temperature uniformity in mesoscale liquids” Sci. Rep. 10 13340 (2020). Doi : 10.1038/s41598-020-69404-1.
3. M. Warburton, J. Ablett, P. Baroni, JP Rueff, L. Paolasini, L. Noirez, “Identification by Inelastic X-Ray scattering of bulk alteration of solid dynamics due to Liquid Wetting”, J. of Molecular Liquids 391 (2023) 123342202
Machine Learning-Based Algorithms for Real-Time Standalone Tracking in the Upstream Pixel Detector at LHCb
This PhD aims to develop and optimize next-generation track reconstruction capabilities for the LHCb experiment at the Large Hadron Collider (LHC) through the exploration of advanced machine learning (ML) algorithms. The newly installed Upstream Pixel (UP) detector, located upstream of the LHCb magnet, will play a crucial role from Run 5 onward by rapidly identifying track candidates and reducing fake tracks at the earliest stages of reconstruction, particularly in high-occupancy environments.
Achieving fast and highly efficient tracking is essential to fulfill LHCb’s rich physics program, which spans rare decays, CP-violation studies in the Standard Model, and the characterization of the quark–gluon plasma in nucleus–nucleus collisions. However, the increasing event rates and data complexity expected for future data-taking phases will impose major constraints on current tracking algorithms, especially in heavy-ion collisions where thousands of charged particles may be produced per event.
In this context, we will investigate modern ML-based approaches for standalone tracking in the UP detector. Successful applications in the LHCb VELO tracking system already demonstrate the potential of such methods. In particular, Graph Neural Networks (GNNs) are a promising solution for exploiting the geometric correlations between detector hits, allowing for improved tracking efficiency and fake-rate suppression, while maintaining scalability at high multiplicity.
The PhD program will first focus on the development of a realistic GEANT4 simulation of the UP detector to generate ML-suitable datasets and study detector performance. The next phase will consist in designing, training, and benchmarking advanced ML algorithms for standalone tracking, followed by their optimization for real-time GPU-based execution within the Allen trigger and reconstruction framework. The most efficient solutions will be integrated and validated inside the official LHCb software stack, ensuring compatibility with existing data pipelines and direct applicability to Run-5 operation.
Overall, the thesis will provide a major contribution to the real-time reconstruction performance of LHCb, preparing the experiment for the challenges of future high-luminosity and heavy-ion running.
Surface technologies for enhanced superconducting Qubits lifetimes
Materials imperfections in superconducting quantum circuits—in particular, two-level-system (TLS) defects—are a major source of decoherence, ultimately limiting the performance of qubits. Thus, identifying the microscopic origin of possible TLS defects in these devices and developing strategies to eliminate them is key to superconducting qubit performance improvement. This project proposes an original approach that combines the passivation of the superconductor’s surface with films deposited by Atomic Layer Deposition (ALD), which inherently have lower densities of TLS defects, and thermal treatments designed to dissolve the initially present native oxides. These passivating layers will be tested on 3D Nb resonators than implemented in 2D resonators and Qubits and tested to measure their coherence time. The project will also perform systematic material studies with complementary characterization techniques in order to correlate improvements in qubit performances with the chemical and crystalline alteration of the surface.
Euclid Weak Lensing Cluster Cosmology inference
Galaxy clusters, which form at the intersection of matter filaments, are excellent tracers of the large-scale matter distribution in the Universe and are a valuable source of information for cosmology.
The sensitivity of the Euclid space mission (launch in 2023) allow blind detection of galaxy clusters through gravitational lensing (i.e. directly linked to the projected total mass). Combined with its wide survey area (14,000 deg²), Euclid should allow the construction of a galaxy cluster catalogue that is unique in both its size and selection properties.
In contrast to existing cluster catalogues, which are typically based on baryonic content (e.g., X-ray emission from intra-cluster gas, the Sunyaev-Zel’dovich effect in the millimeter regime, or optical emission from galaxies), a catalogue derived from gravitational lensing is directly sensitive to the total mass of the clusters. This makes it truly representative of the underlying cluster population, a significant advantage for both galaxy cluster studies and cosmology.
In this context, we have developed a multi-scale detection method specifically designed to identify galaxy clusters based only on their gravitational lensing signal, which has been pre-selected to produce the Euclid cluster catalogue.
The goal of this PhD project is to build and characterize the galaxy cluster catalogue identified via weak lensing in the data collected during the first year of Euclid observations (DR1), based on this detection method. The candidate will derive cosmological constraints from the modelling of the cluster abundance, using the classical Bayesian framework, and will also investigate the potential of Simulation-Based Inference (SBI) methods for cosmological inference.
Bayesian Neural Networks with Ferroelectric Memory Field-Effect Transistors (FeMFETs)
Artificial Intelligence (AI) increasingly powers safety-critical systems that demand robust, energy-efficient computation, often in environments marked by data scarcity and uncertainty. However, conventional AI approaches struggle to quantify confidence in their predictions, making them prone to unreliable or unsafe decisions.
This thesis contributes to the emerging field of Bayesian electronics, which exploits the intrinsic randomness of novel nanodevices to perform on-device Bayesian computation. By directly encoding probability distributions at the hardware level, these devices naturally enable uncertainty estimation while reducing computational overhead compared to traditional deterministic architectures.
Previous studies have demonstrated the promise of memristors for Bayesian inference. However, their limited endurance and high programming energy pose significant obstacles for on-chip learning applications.
This thesis proposes the use of ferroelectric memory field-effect transistors (FeMFETs)—which offer nondestructive readout and high endurance—as a promising alternative for implementing Bayesian neural networks.
Adaptive and explainable Video Anomaly Detection
Video Anomaly Detection (VAD) aims to automatically identify unusual events in video that deviate from normal patterns. Existing methods often rely on One-Class or Weakly Supervised learning: the former uses only normal data for training, while the latter leverages video-level labels. Recent advances in Vision-Language Models (VLMs) and Large Language Models (LLMs) have improved both the performance and explainability of VAD systems. Despite progress on public benchmarks, challenges remain. Most methods are limited to a single domain, leading to performance drops when applied to new datasets with different anomaly definitions. Additionally, they assume all training data is available upfront, which is unrealistic for real-world deployment where models must adapt to new data over time. Few approaches explore multimodal adaptation using natural language rules to define normal and abnormal events, offering a more intuitive and flexible way to update VAD systems without needing new video samples.
This PhD research aims to develop adaptable Video Anomaly Detection methods capable of handling new domains or anomaly types using few video examples and/or textual rules.
The main lines of research will be the following:
• Cross-Domain Adaptation in VAD: improving robustness against domain gaps through Few-Shot adaptation;
• Continual Learning in VAD: continually enriching the model to deal with new types of anomalies;
• Multimodal Few-Shot Learning: facilitating the model adaptation process through rules in natural language.
Theoretical studies of orbital current and their conversion mechnism for leveraging spin-orbit torques based devices performances
The proposed PhD thesis aims at understanding and identifying the key parameters governing the conversion of orbital moments into spin currents, with the goal of enhancing the write efficiency of spin-orbit torque magnetic random-access memory (SOT-MRAM) devices. The work will employ a multiscale modeling approach comprising ab initio, tight-binding and atomistic calculations of the Orbital Hall Effect (OHE) and Orbital Rashba-Edelstein Effect (OREE). These phenomena exhibit larger magnitudes and diffusion lengths compared to their spin counterparts, Spin Hall Effect (SHE) and Rashba-Edelstein Effect (REE). Furthermore, they are present in a broader range of materials, including low-resistivity light metals. This opens very interesting opportunities for more efficient and conductive materials, potentially lifting the barriers limiting the technological deployment of SOT-MRAM.
This thesis will play a key role in a close collaboration between SPINTEC and LETI laboratories at CEA. The PhD student will conduct ab initio calculations at SPINTEC to unveil fundamental material characteristics to exploit the described orbitronic phenomena, and will construct multi-orbital Hamiltonians at LETI to calculate orbital and spin transport, in strong interaction/synergy with experimentalists working on SOT-MRAM development. The PhD will be co-supervised by M. Chshiev, K. Garello at Spintec and J. Li at LETI. This PhD project will be at the heart of collaborations with leading theoretical and experimental groups at national and international level.
Highly motivated candidates with a strong background in solid-state physics, condensed matter theory, and numerical simulations are encouraged to apply. The selected candidate will perform calculations using Spintec’s computational cluster, leveraging first-principles DFT-based packages and other simulation tools. Results will be rigorously analyzed, with opportunities for publication in international peer-reviewed journals.
Blended positive electrodes in solid-state batteries: Effect of the electrode fabrication process on electrochemistry
The development of cost-effective, high-energy-density solid-state batteries (SSBs) is essential for the large-scale adoption of next-generation energy storage technologies. Among various cathode candidates, LiFePO4 (LFP) and LiFe1??Mn?PO4 (LFMP) offer safety and cost advantages but suffer from low working voltages and limited kinetics compared to Ni-rich layered oxides such as LiNi0.85Mn0.05Co0.1O2 (NMC85). To balance energy density, rate capability, and stability, this PhD project aims to develop blended cathodes combining LFMP and NMC85 in optimized ratios for solid-state configurations employing sulfide electrolytes (Li6PS5Cl). We will investigate how fabrication methods- including slurry-based electrode processing and binder-solvent optimization- affect the electrochemical and structural performance. In-depth operando and in situ characterizations (XRD, Raman, and NMR) will be conducted to elucidate lithium diffusion, phase transition mechanisms, and redox behavior within the blended systems. Electrochemical impedance spectroscopy (EIS) and titration methods will quantify lithium kinetics across various states of charge. By correlating processing conditions, microstructure, and electrochemical behavior, this research seeks to identify optimal cathode compositions and manufacturing strategies for scalable, high-performance SSBs. Ultimately, the project aims to deliver a comprehensive understanding of structure–property relationships in blended cathodes, paving the way for practical solid-state battery technologies with enhanced safety, stability, and cost efficiency.