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.
Evaluation of the impact of dry extrusion process on cathode microstructure and performances for polymer-based solid-state batteries
Solid-state batteries (SSB) are expected to outperform standard lithium-ion technology in terms of energy density and safety, with application in electric vehicles or stationary energy storage. Manufacturing of these new battery technologies can rely on existing infrastructure (solvent-based electrode slurry mixing and coating) or need new processing methods. In this context, twin-screw extrusion process exhibits several advantages when applied to SSB, particularly with polymer-based electrolytes.
To speed up the implementation of polymer-based SSB, a better understanding of extrusion process applied to positive electrode manufacturing is needed. The objective of this thesis is to develop new electrode formulations using hot-melt extrusion and understand the impact of process parameters on final performances. It should finally give a clear picture about the advantages and limitations of extrusion compared to standard wet casting.
This PhD project will be part of a collaboration between CEA and Stellantis on the development of new solid-state batteries. The study will focus on the development of extrusion-processed composite electrodes to be used in polymer-based SSB. First, materials will be selected and characterized for a preliminary screening of formulations using lab-scale extrusion. Then, a systematic evaluation of the impact of input materials and operational conditions during extrusion process will be undertaken to highlight the relationships between process, electrode microstructures and performances. Finally, the best performing electrode formulations will be integrated in a fully-extruded prototype and characterized by electrical tests as well as post-mortem analysis.
The PhD candidate will benefit from CEA-LITEN's multidisciplinary environment (Grenoble campus) and Stellantis industrial know-how. Battery Prototyping Platform will be used for extrusion trials and cell assembly, whereas access to advanced characterization equipment (SEM, XPS, rheometers, electrochemical methods, etc.) will guarantee deep understanding of underlying mechanisms.
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.
Structure monitoring in harsh environments: fiber Bragg gratings for passive guided wave tomography
The use of fiber Bragg gratings on optical fiber as receivers of guided elastic waves has been studied for several years at CEA LIST as an innovative solution for monitoring structures subjected to severe operational stresses.
Recent advances in optoelectronic instrumentation dedicated to this type of measurement have demonstrated the team's ability to measure elastic waves at temperatures exceeding 1000°C and to achieve degrees of multiplexing on a single optical fiber that enable the implementation of guided elastic wave tomography algorithms. In addition, a model of elastic waves measurement using fiber Bragg gratings has recently been introduced into CIVA simulation platform developed by CEA LIST. This model will be used in order to adapt the tomography algorithms, developed and tested for “standard” piezoelectric sensors, to the specific characteristics of Bragg measurements.
This thesis will be take place in parallel to experimental campaigns planned as part of European projects and industrial collaborations, which will enable this type of instrumentation to be implemented on real industrial structures in 2027/2028 (especially nuclear power plants), providing unique data for analysis.
The doctoral student will work on purely algorithmic aspects (adapting tomography algorithms to the specificity of Bragg measurement, taking into account geometric complexities on real industrial structures, calibration issues related to high temperatures/gradients) and on the development of demonstrators in the laboratory. He or she will also participate in the deployment of the instrumentation on industrial sites and in data analysis to demonstrate the performances of the technology.
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.
Sofware support for computing accelerators and memory transferts accelerators
For energy reasons, future computers will have to use accelerators for both computation and memory access (GPUs, TPUs, NPUs, smart DMAs). AI applications have intensive computational requirements in terms of both computing power and memory throughput.
These accelerators are not based on a simple instruction set (ISA), they break the Von Neuman model: they require specialized code to be written manually.
Furthermore, it is difficult to compare the use of these accelerators with code using a non-specialized processor, as the initial source codes are very different.
HybroLang is a hardware-close programming language that allows programs to be written using all of a processor's computing capabilities, while also allowing code to be specialized based on data known at runtime.
The HybroGen compiler has already demonstrated its ability to program in-memory computing accelerators, as well as to optimize code on conventional CPUs by performing innovative optimizations.
This thesis proposes to extend the HybroLang language in order to
- facilitate the programming of AI applications by providing support for complex data: stencils, convolution, sparse computing
- enable code generation both on CPUs and with hardware accelerators currently under development at the CEA (sparse computing, in-memory computing, memory access)
- allow to benchmark different computing architectures with the same initial source code
Ideally, a candidate should have knowledge of computer architecture, programming language implementation, code optimization and compilation.
LLM-Assisted Generation of Functional and Formal Hardware Models
Modern hardware systems, such as RISC-V processors and hardware accelerators, rely on functional simulators and formal verification models to ensure correct, reliable, and secure operation. Today, these models are mostly developed manually from design specifications, which is time-consuming and increasingly difficult as hardware architectures become more complex.
This PhD proposes to explore how Large Language Models (LLMs) can be used to assist the automatic generation of functional and formal hardware models from design specifications. The work will focus on defining a methodology that produces consistent and executable models while increasing confidence in their correctness. To achieve this, the approach will combine LLM-based generation with feedback from simulation and formal verification tools, possibly using reinforcement learning to refine the generation process.
The expected outcomes include a significant reduction in manual modeling effort, improved consistency between functional and formal models, and experimental validation on realistic hardware case studies, particularly RISC-V architectures and hardware accelerators.
Study of the Metastability of Silicon Heterojunction Solar Cells and Stabilization Strategies
Silicon-based photovoltaic cells, particularly silicon heterojunction (SHJ) cells using hydrogenated amorphous silicon (a-Si:H), achieve efficiencies exceeding 25%. However, these architectures exhibit intrinsic metastability, such as Staebler-Wronski degradation, which can lead to efficiency losses during storage between fabrication and module assembly. In the context of globalized supply chains, these instabilities represent an economic and technical risk that is not yet well quantified. This thesis aims to address the following questions: what is the quantitative impact of instability on the efficiency of high-efficiency cells during prolonged storage? What are the physical mechanisms responsible for this degradation? What technological strategies can reduce or eliminate this instability? What are the industrial implications for module logistics? To achieve this, a rigorous experimental protocol will be implemented to monitor the electrical performance of cells over several months under varying storage conditions (atmosphere, temperature, humidity). Test structures and advanced characterizations (FTIR, Raman, Silvaco TCAD) will be used to understand the underlying physical phenomena. Process optimization, introduction of new materials, and improved packaging will be explored to stabilize the cells. Practical recommendations for the industry, regarding maximum storage durations and optimal storage conditions, will also be established. The goal is to develop technological and logistical solutions to minimize efficiency losses in SHJ cells, optimize supply chains, and reduce associated economic risks.
Architecture of small animal single photon emission tomograph.
Medical imaging, a source of major innovations, presents remarkable potential for meeting new challenges with the growing demand for precision medicine, which requires cutting-edge diagnostic and therapeutic approaches personalized for each patient.
In this context, CEA-Leti proposes a PhD internship to develop a dedicated preclinical SPECT (Single Photon Emission Tomography) imager that will provide the performance (spectral information, high resolution, and high sensitivity) needed by researchers developing new radiopharmaceuticals.
The laboratory has a recognized expertise on CZT (Cadmium Zinc Telluride) semiconductor imagers enabling better spatial and energy resolution than scintillators used by most systems. They open new opportunities for emission imaging like easier Compton imaging, multi-isotope imaging and better contrast.
The candidate will have to handle the following tasks:
1. Study the state of the art of small animal SPECT imagers to participate with the team to the choice of system specification and choice of a draft architecture.
2. Simulate this architecture by using Monte-Carlo codes and optimize free parameters.
3. Design and manufacture the prototype system, with the help of the team including system engineers.
4. Test and validate the imaging capabilities, using reconstruction algorithms provided by the team.
The PhD will be conducted inside an instrumentation laboratory with access to acquisition electronics, detectors, motorized mechanics, gamma-ray sources and processing/simulation software. The candidate will also work in collaboration with a clinical and preclinical centre (at Orsay’s hospital) for conducting imaging test on phantoms and animals.