High-throughput screening of catalysts for the direct conversion of CO2 into synthetic fuels
This doctoral project aims to develop an innovative high-throughput screening approach for catalysts for the direct conversion of CO2 into synthetic fuels, known as CO2-FTS. This approach will combine a catalyst screening platform with in situ/operando characterization techniques and artificial intelligence methods to accelerate the discovery and optimization of high-performance catalysts. It aims to identify doped FeOx-type catalysts for the CO2-FTS reaction (>50% conversion, high selectivity towards C8-C16). Several high-throughput screening campaigns will allow for iterative optimization of compositions and reactive conditions. A numerical model of the parametric landscape will then be developed. This model will subsequently be coupled with multi-scale modeling from the active site to the reactor level. The developed catalysts will contribute to the energy transition by enabling a circular carbon economy.
Lightweight CNN and Causal GNN for scene understanding
Scene understanding is a major challenge in computer vision, with recent approaches dominated by transformers (ViT, LLM, MLLM), which offer high performance but at a significant computational cost. This thesis proposes an innovative alternative combining lightweight convolutional neural networks (Lightweight CNN) and causal graph neural networks (Causal GNN) for efficient spatio-temporal analysis while optimizing computational resources. Lightweight CNNs enable high-performance extraction of visual features, while causal GNNs model dynamic relationships between objects in a scene graph, addressing challenges in object detection and relationship prediction in complex environments. Unlike current transformer-based models, this approach aims to reduce computational complexity while maintaining competitive accuracy, with potential applications in embedded vision and real-time systems.
Reinventing Microspeakers: From Planar Limits to 3D Designs for Ultrasonic Modulation Loudspeakers
Are you looking for a PhD at the intersection of acoustics, microsystems, and innovation? This project may be for you.This PhD focuses on the design, fabrication, and experimental validation of an innovative MEMS microspeaker concept based on ultrasound demodulation. Conventional micro transducers face a major limitation: they require large planar surfaces to displace sufficient air at low frequencies, leading to increased device size and manufacturing cost. This project explores an alternative architecture using vertical blade structures, exploiting the third dimension together with ultrasound demodulation to improve electro acoustic efficiency while reducing device footprint.
Building on preliminary exploratory work, the objective of the PhD is to develop a complete MEMS loudspeaker demonstrator. The work will include physical modeling, multi-physics simulation, device design optimization, microfabrication process development, and experimental electro acoustic characterization. Particular attention will be given to identifying and overcoming the physical and technological limitations governing device performance.
The candidate will design and simulate the device architecture and contribute to the definition of the fabrication process in close interaction with microfabrication specialists. The PhD work will also include acoustic and electrical characterization of the fabricated devices in order to validate the proposed concepts and compare experimental results with modeling predictions. The PhD will take place in a multidisciplinary environment, providing access to expertise in acoustics, MEMS design, microfabrication processes, and electro acoustic measurement.
Dies to wafer direct bonding: from physical mechanisms to the development of thin stackable dies
Direct dies-to-wafer bonding has become, in recent years, a major development axis in microelectronics and at the heart of many LETI projects, both in silicon photonics and for 3D applications involving hybrid bonding.
Due to their small size, die bonding allows the study of direct bonding edge effects and the implementation of new direct bonding processes that can shed original light on the mechanisms of direct bonding, which are already well studied at LETI. From a more technological perspective, the development of thin stackable chips will also be a very interesting technological key for many applications. This approach is a clever alternative to classical damascene processes to address the challenges related to the planarization of surfaces with low density of high topographies.
Resilience of fusion plasmas in a metallic environment, from WEST to ITER
Magnetic confinement nuclear fusion is an attractive option for contributing to the future energy mix, and the ITER project will, in the coming decade, mark a new milestone in the scientific and technological development of this field by producing more fusion energy than the energy deposited to sustain it. However, in a fusion power plant, the wall of the combustion chamber will be subjected to strong thermal and neutron stresses and must also limit the trapping of hydrogen isotopes used in the nuclear reaction.
The material considered the best compromise is tungsten, a metal whose high melting point and lack of chemical affinity with hydrogen are its main advantages. However, its high atomic number makes it highly radiative in the plasma where the reactions occur, which is detrimental to energy confinement and overall performance. It is therefore crucial to understand—both on current machines and through simulations for ITER—the impact of the inevitable tungsten dust (observed in the WEST tokamak) on turbulent transport, magneto-hydrodynamic stability, and ultimately on achieving a viable scenario for nuclear fusion. These aspects will form the foundation of the PhD project, combining experimental analysis on WEST at CEA with validation through simulations that include all relevant aspects, and extrapolation to the ITER environment. This work will be carried out in collaboration with ITER, the UKAEA (United Kingdom) for the simulation code, the CNR-Milano team for the tungsten dust trajectory, and the CEA teams at the IRFM.
Hybrid CPU-GPU Preconditioning Strategies for Exascale Finite Element Simulations
Exascale supercomputers are based on heterogeneous architectures that combine CPUs and GPUs, making it necessary to redesign numerical algorithms to fully exploit all available resources. In large-scale finite element simulations, the solution of linear systems using iterative solvers and algebraic multigrid (AMG) preconditioners remains a major performance bottleneck.
The objective of this PhD is to study and develop hybrid preconditioning strategies adapted to such heterogeneous systems. The work will investigate how multilevel and AMG techniques can be structured to efficiently use both CPUs and GPUs, without restricting computations to a single type of processor. Particular attention will be paid to data distribution, task placement, and CPU–GPU interactions within multilevel solvers.
From a numerical point of view, the research will focus on the analysis and construction of multilevel operators, including grid hierarchies, intergrid transfer operators, and smoothing procedures on avalible GPU's and CPU's. The impact of these choices on convergence, spectral properties, and robustness of preconditioned iterative methods will be studied. Mathematical criteria guiding the design of efficient hybrid preconditioners will be investigated and validated on representative finite element problems, e.g., regional-scale earthquake analysis.
These developments will be coupled with domain decomposition and parallelization strategies adapted to heterogeneous architectures. Particular attention will be paid to CPU–GPU data transfers, memory usage, and the balance between compute-bound and memory-bound kernels. The interaction between numerical choices and hardware constraints, such as CPU and GPU memory hierarchies, will be designed and developed to ensure scalable and efficient implementations.
Hydrodynamic simulations of porous materials for ductile damage
The mechanical behavior of metallic materials under highly dynamical loading (schock) and especially their damage behavior is a topic of interest for the CEA-DAM. For tantalum, damage is ductile : by nucleation, growth and coalescence of voids within the material. Usual ductile damage models have been developed using the simplifying assumption that voids are isolated in the materials. However, recent studies by direct simulations explicitly describing a void population in the material (and experimental observations after failure) have shown the importance of void interaction for predicting ductile damage. Yet, the microscopical mechanisms of this interaction remain little known.
The objective of the PhD is to study the growth and coalescence phases of ductile damage through direct numerical simulations of a porous material undergoing dynamic loading. Hydrodynamic simulations, in which voids are explicitly meshed within a continuous matrix, will be used to study relevant scales of length and time. Monitoring the void population throughout the simulation will provide valuable information on the influence of void interaction during ductile damage. Firstly, the bulk behavior will be compared to the one predicted by usual models of isolated voids, showing the macroscopic effect of void interaction. Secondly, the evolution of the size distribution in the void population will be monitored. The last objective will be to understand microscopic void-to-void interaction. In order to take advantage of the wealth of simulation results, approaches based on artificial intelligence (neural networks on the graph associated with the pore population) will be used to learn the link between a void's neighborhood and its growth.
The doctoral student will have the opportunity to develop their skills in shock physics and mechanics, numerical simulations (with access to CEA-DAM supercomputers), and data science.
Control & optimization of fuel cell temperature
Proton exchange membrane fuel cells (PEMFC) represent a key technology for the development of clean and sustainable energy systems, particularly for heavy-duty transport applications where their energy density is very attractive. However, in order to represent a viable industrial alternative, a number of obstacles still need to be overcome, including operating costs and, above all, the durability of the systems under real-world conditions. Among the levers for action, optimizing operating conditions is a promising avenue for limiting the degradation phenomena occurring within the cell. The operating temperature is a particularly key parameter because it affects all aspects of the system, from the kinetics of degradation mechanisms to the thermal capacity that the system can dissipate, including the water balance within the fuel cell. Despite the influence of this parameter on durability, it is generally only optimized at the system level to achieve the best performance, the shortest possible response time and to limit the size of the thermal management system.
The aim of this thesis is to work on optimizing the temperature management of a fuel cell within a system, taking into account not only performance but also sustainability criteria. To do this, the impact of operating temperature on degradation mechanisms will be analyzed using various simulation tools already available at LITEN and the teams' fifteen years of experience in studying PEMFC fuel cell degradation. Various thermal architectures will be proposed and evaluated in conjunction with the work on temperature control optimization. The latter will be implemented on a real fuel cell system in order to demonstrate the relevance of the proposed solution using concrete experimental data.