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.
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.
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.
Growth of 2D Ferromagnetic Chalcogenide Materials for Spintronics
Chalcogenide materials, particularly Ge-Sb-Te (GST) alloys, are essential for phase-change memory (PCMs).
Although high-performance, these memories consume a great deal of energy, which
is driving the search for alternative solutions. GST alloys offer unique opportunities in the field of spin-orbitronics as spin-charge conversion materials or as sources of spin-polarized current. Two-dimensional ferromagnetic alloys such as Fe-Ge-Te or Ge-Mn-Te offer promising avenues as sources of spin current for new types of more efficient memory devices. For efficient spin injection, we are seeking a material that not only exhibits a high Curie temperature (TC) and significant spin polarization, but is also fully compatible with existing silicon-based CMOS technology.
The aim of this thesis is to develop and master, on an industrial scale on 300 mm Si substrates, the van der Waals epitaxial growth of 2D ferromagnetic films based on Fe-Ge (Ga)Te2 (n=3, 5) or Ge_(1-x)Mn_xTe, for example to integrate them in situ with spin-charge conversion chalcogenide layers such as ferroelectric layers (a-GeTe(111)) or topological insulators (Bi_(2-x)Sb2Te3).
Development of Machine Learning algorithm to optimize the control of absorption machines
The Thermal and Solar Technologies Laboratory (L2TS) and the Energy Systems for Territories Laboratory (LSET), located at the CEA LITEN site in Le Bourget-de-Lac, are offering a cross-disciplinary PhD thesis combining thermodynamics and optimization using Artificial Intelligence.
Specifically, this doctoral research project involves developing a machine learning algorithm to optimize the control of absorption machines. These machines are thermodynamic cycles able to produce heat or cold from an intermediate heat input; thus, offering potential valorization of industrial waste heat or renewable energies, such as solar thermal. Heat exchange is made possible by the absorption and desorption reactions of a gaseous refrigerant in a fluid. Specifically, the NH3-H2O mixture will be used. The dynamic operation of these cycles is extremely complex because the operational variables, physical parameters, and hydrodynamic aspects are highly intertwined. Thus, the use of a neural network is particularly relevant for establishing an adaptive control strategy for these machines.
The thesis will have a theoretical aspect, involving the study and selection of the most suitable algorithm to address the problem, and an experimental aspect of validation on a prototype absorption machine. The project will also involve the design of a controller for implementation.
The thesis will take place in a CEA laboratory in Bourget du Lac.
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.
Development and characterization of a low-silver metallization for photovoltaic cells with high-efficiency passivated contacts
In order to decarbonize energy production and meet climate plan objectives, the production of photovoltaic (PV) modules must increase significantly. To sustain these production levels, the silver content in latest-generation cells must be drastically reduced. Some alternatives incorporate less expensive metals (nickel, aluminum, copper) into screen-printing pastes. These approaches require evaluation in terms of contact formation, electron transport, and reliability. In a TOPCon cell architecture, the electrode must be brought into direct contact with the active layers of the cell via thermal annealing. This step enhances device performance (through a hydrogenation phenomenon) while simultaneously generating potential degradation related to the introduction of metallic species. This is especially critical when using new metals (Ni, Cu, etc.) with higher diffusivities than silver. The objectives of this thesis are manifold: to evaluate the performance of these low-silver alternative pastes once integrated into TOPCon cells; to characterize the impact of the introduction of these metallic species on the lifetime of photogenerated carriers in silicon; and to assess the long-term stability of these metallizations while verifying the absence of cell degradation phenomena under prolonged illumination. If necessary, an alternative metallization technique more suitable for these pastes will be developed. During the PhD, the successful candidate will be required to fabricate, metallize via screen printing, and characterize devices within a cleanroom environment.