Post-training neural architecture optimization for small language models
Generative AI, and particularly language models (LLM), have sparked a new revolution in AI with applications across all domains. However, LLMs are highly resource-intensive and, hence, difficult to implement on autonomous embedded systems. LLMs can be optimized by modifying their architecture to replace heavy Transformer layers with lighter alternatives. Given the difficulty of training LLM "from scratch," this thesis aims to develop post-training neural architecture optimization methods applicable to small LLM (SLM). Additionally, the thesis seeks to propose performance metrics of different layers of an SLM and their alternatives, to guide the replacement, and thus propose a comprehensive methodology for optimizing SLMs while considering hardware constraints. The work will be valorized through publications in major AI conferences and journals, and the developed codes and methods could be integrated into the tools developed at CEA.
Development of an innovative anode based on non-critical and sustainable materials for anion-exchange membrane electrolysis
Anion-exchange membrane water electrolysis (AEMWE) is a recent and promising technology for producing green hydrogen, but it still faces major challenges in terms of performance and durability. Currently, the anodes used in AEMWE electrolyzers consist of two layers: a porous transport layer (PTL), which enables the circulation of electrolyte and gases, and an active layer made of catalysts and binders, where the electrochemical reactions take place. This configuration limits reactant diffusion and reduces the available active surface area, which negatively impacts overall performance.
This PhD project aims to develop an innovative anode based on non-critical materials by combining the advantages of both layers while minimizing their drawbacks. The idea is to functionalize the PTL directly by adding catalyst nanoparticles and/or by applying a surface activation treatment, in order to confer electrochemical activity. These modifications are expected to improve electron and reactant transport while increasing the active surface area for the oxygen evolution reaction (OER).
The work carried out in this thesis will involve functionalizing a pre-selected PTL and characterizing the resulting anodes through structural and electrochemical analyses. The expected outcomes include the development of an optimized anode with enhanced performance and limited degradation, as well as a deeper understanding of the limiting phenomena in AEMWE anodes. This project is part of a broader effort to develop sustainable technologies essential for the energy transition.
An electrochemical flow microreactor for a greener synthesis of gold nanoparticles
Gold nanoparticles (AuNPs) possess unique electronic, photonic, and chemical properties of invaluable interest in a variety of medical and technological applications. They are typically produced by controlled chemical precipitation from a salt solution to achieve the precise size control critical for most applications. Continuous flow microreactors, which efficiently mix the salt solution and the reducing agent, are known to offer improved size control. However, even in these reactors, the smallest AuNPs can only be formed using powerful reducing agents that are harmful to human health or the environment. We propose to minimize their impact and to develop a more resource-efficient process by inserting an electrochemical cell into the reactor to form the reducing agent in-situ in the adjusted amount necessary to produce the desired AuNPs.
Your goal will be to test and adapt continuous-flow electrochemical cells for the synthesis of AuNPs, exploring various electrochemical reactions and cell designs. You will also explore the use of several capping agents of biological interest. A careful examination of AuNPs characteristics (size, interfacial and optical properties, etc.) will guide you in this research.
Multi-scale approach for ultrasonic propagation in inhomogeneous multiple-scattering media
Ultrasonic waves are strongly influenced by the microstructure of the materials through which they propagate, leading to attenuation, dispersion, and noise. Modeling these effects is essential, particularly in non-destructive testing, where they may either hinder defect detection or provide valuable information about the material. Analytical and numerical models help to better predict and interpret these phenomena. Homogeneous statistical properties are generally assumed in such approaches. In practice, however, microstructures often exhibit significant spatial variations, for instance due to manufacturing processes. Depending on the scale of these variations relative to the wavelength, they may induce either abrupt or gradual changes in effective properties. This PhD aims to establish a theoretical framework that accounts for both microstructural randomness and its spatial variations, in order to propose relevant simulation strategies depending on the scales involved. The approach will first be developed in 1D, then extended to 2D and 3D using tools developed in the laboratory, with numerical and possibly experimental validations.
Control coordination of power converters on the distribution grid to enhance overal system stability
With the increasing number of generation and consumption units connected through power electronic converters, the electrical grid is evolving toward a more dynamic and decentralized structure. This transformation strengthens both the need and the potential for these converters to actively contribute to system flexibility and stability—particularly in compensating for renewable energy fluctuations and maintaining the balance between supply and demand.
Optimized coordination of their control functions offers significant potential to improve grid resilience, by intelligently leveraging their capabilities in voltage regulation, frequency support, and reactive power control. However, to integrate these contributions effectively at scale, it is essential to develop holistic modeling approaches that capture multi-scale interactions—both in time and space.
The modeling work in this thesis aims to represent the relationship between the active/reactive power flexibility of power electronic converters and the stability margin they provide to the grid, as well as to model the aggregation of their actions for system-wide contribution. Building on this foundation, coordinated control architectures and algorithms between the distribution and transmission networks will be investigated, developed, and validated.
Prediction of elastic wave dispersion effects using a semi-analytical model under high-frequency approximation
Ultrasonic testing (UT) methods are a fundamental component of non-destructive testing (NDT). They are widely used to inspect mechanical components such as welds (in nuclear and petrochemical industries) and composite material structures (in aeronautics). To understand the physical phenomena involved in a given configuration, simulation is a valuable tool and sometimes an essential step in implementing the inspection process.
Modeling approaches fall into two main categories: purely numerical models based on finite elements (FE) and semi-analytical methods derived from high-frequency (HF) approximations, such as paraxial rays. While the latter are often favored for their computational efficiency, they introduce simplifications that can compromise the quantitative accuracy of results, particularly for phenomena like dispersion (variation in wave speed with frequency), which are common in certain industrial contexts.
This thesis project aims to enhance the paraxial ray approach by integrating models of dispersive interfaces (composite interplies, coupling layers), dispersive viscoelastic media, and a modal guided wave model. The goal is to develop a simulation tool capable of faithfully reproducing realistic inspection configurations, thereby improving the representativeness of the results.
Reconciling predictability and performance in processor architectures for critical systems
Critical systems have both functional and timing requirements, the latter ensuring that deadlines are always met during operation; failure to do so may lead to catastrophic consequences. The critical nature of such systems demands specialized hardware and software solutions. This PhD thesis topic focuses on the development of computer architecture designs for critical systems, known as predictable architectures, capable of providing the necessary timing guarantees. Several such architectures exist, typically based on in-order pipelines and incorporating behavioral restrictions (e.g., disabling complex speculation mechanisms) or structural specializations (e.g., redesigned caches or deterministic arbitration for shared resources). These restrictions and specializations inevitably impact performance, and the design of predictable architectures must therefore address the predictability–performance tradeoff directly. This PhD thesis aims to explore this tradeoff in a novel way, by adapting a high-performance variant of an in-order processor (CVA6) and developing top-down techniques to make it predictable. Performance in such processors is usually achieved through mechanisms like branch prediction, prefetching, and value prediction, implemented via specialized storage elements (e.g., buffers) and supported by control mechanisms such as rollback on misprediction. Within this context, the goal of the thesis is to define a general predictability scheme for speculative execution, covering both storage organization and rollback behavior.
Out-of-Distribution Detection with Vision Foundation Models and Post-hoc Methods
The thesis focuses on improving the reliability of deep learning models, particularly in detecting out-of-distribution (OoD) samples, which are data points that differ from the training data and can lead to incorrect predictions. This is especially important in critical fields like healthcare and autonomous vehicles, where errors can have serious consequences. The research leverages vision foundation models (VFMs) like CLIP and DINO, which have revolutionized computer vision by enabling learning from limited data. The proposed work aims to develop methods that maintain the robustness of these models during fine-tuning, ensuring they can still effectively detect OoD samples. Additionally, the thesis will explore solutions for handling changing data distributions over time, a common challenge in real-world applications. The expected results include new techniques for OoD detection and adaptive methods for dynamic environments, ultimately enhancing the safety and reliability of AI systems in practical scenarios.
Heat Transfer Enhancement by Convective Boiling in Microchannels applied to the Cooling of Computing Units in Data Centers
The proposed PhD thesis aims to improve the understanding and modeling of convective boiling phenomena in microchannels for new low-environmental-impact refrigerants. The candidate will adopt a combined experimental and multi-scale modeling approach, including the design of a test bench simulating the behavior of a micro-evaporator, the implementation of CFD simulations (ANSYS Fluent, CATHARE) to describe two-phase flow regimes, and the evaluation of various eco-friendly alternative fluids. The expected outcomes include, for each of these new fluids, the characterization of confined boiling mechanisms, the development of a predictive heat transfer model, and the proposal of innovative cooling solutions.
The growing demand for high-performance computing, driven by artificial intelligence and cloud technologies, leads to a significant increase in power dissipation in electronic chips. Current single-phase cooling technologies are reaching their limits when dealing with heat fluxes exceeding 100 W/cm². Two-phase cooling, based on fluid boiling to remove heat, can achieve much higher heat transfer performance than single-phase systems while reducing overall energy consumption. The results of this research will contribute to the development of more efficient and sustainable cooling solutions for future data centers, helping to reduce the digital sector’s energy footprint and strengthen European technological sovereignty in advanced cooling technologies.
Advancing Lithium-Sulfur Batteries through the study of the Quasi-Solid Sulfur Conversion
Lithium–sulfur batteries are widely seen as one of the most promising candidates for the next generation of energy storage, offering the potential for significantly higher energy density than today’s batteries while using abundant and inexpensive sulfur. However, several scientific and technological challenges still prevent their large-scale industrial deployment.
One key issue is the formation of soluble lithium polysulfides during battery operation, which can migrate inside the cell and lead to rapid capacity loss. Recent research suggests that a different reaction pathway, known as a “quasi-solid mechanism”, could limit this dissolution and significantly improve battery stability.
This PhD project aims to design and study lithium–sulfur pouch cells operating through this quasi-solid mechanism. The work will combine materials development, electrochemical testing, and advanced characterization techniques to better understand the processes governing battery performance and durability.
The project will focus on two complementary research directions:
1. Design of advanced sulfur cathodes
The first part of the work will involve developing optimized sulfur-based cathodes. This includes exploring different conductive host materials and tuning their structure and surface properties to better confine sulfur and reduce unwanted reactions.
2. Development of improved electrolytes
The second part of the project will focus on electrolyte formulations that reduce the solubility of polysulfides while maintaining good battery performance. Current solutions often rely on dense, fluorinated solvents that increase cost and environmental impact. This project will explore alternative solvent systems and investigate how salt composition and concentration influence cell behaviour.
To gain deeper insight into the quasi-solid reaction mechanism, the project may also involve operando or in-situ characterization techniques, such as Raman spectroscopy, X-ray diffraction, and high-resolution X-ray tomography.