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.
Few-shot event and complex relation extraction from text applied to scientific literature
Information extraction from text, which falls under the broader field of Natural Language Processing, has been the subject of research for many years. These efforts have primarily focused on Named Entity Recognition, relation extraction between entities, and, in its most complex form, event extraction, a task typically formulated as filling predefined templates from unstructured text. Within this framework, the objective of this thesis is to design, develop, and evaluate event extraction models operating on scientific articles. In this context, an "event" may correspond to a set of entities and relations characterizing, for instance, a chemical reaction or an experiment. Furthermore, these models must be capable of being defined from a highly restricted set of annotated data to allow for rapid adaptation to new scientific domains.
From a methodological standpoint, the proposed thesis seeks to move beyond the current, almost reflexive tendency to rely exclusively on Large Language Models (LLMs). Instead, it advocates for a potential synergy between LLMs and smaller encoder-based models within a few-shot context. In this synergy, the former are leveraged, through the generation of synthetic data and annotations, to build the resources necessary to implement the latter via pre-training mechanisms. This thesis will be conducted within the framework of the AIKO project of the Digital Programs Agency, which focuses on knowledge extraction from scientific publications.
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.
Accelerated development of Zn-MnO2 technology for long-term storage through simulation-data hybridization
The massive deployment of renewable energies is driving increasing demand for stationary energy storage, whose specific characteristics (cost, safety, durability) differ radically from those of electric mobility. Faced with the limitations of Li-ion batteries (fire risks, criticality of lithium and cobalt, production costs), aqueous zinc-manganese (Zn-MnO2) technology is emerging as a disruptive alternative. Based on abundant, non-toxic, and inherently safe materials, it offers unique potential for long-term storage with a low environmental impact.
However, the industrialization of this technology faces scientific hurdles that limit reversibility and cycle life, notably the formation of zinc dendrites and cathode instability. This doctoral project proposes to overcome these obstacles through a hybrid research strategy combining multiphysics modeling and artificial intelligence.
Initially, a finite element model will be developed and experimentally validated to characterize degradation mechanisms (current density hotspots, concentration gradients). Subsequently, this model will serve as a data generator to train machine learning algorithms. These surrogate models will enable the rapid exploration of a vast design space to identify the most resilient architectures. The ultimate goal is to accelerate the eco-design of high-performance Zn-MnO2 batteries that meet the imperatives of energy sovereignty and the circular economy.