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.
Sustainable development of digital circuits and systems: Taking planetary boundaries into account
Technological developments in the electronics sector are experiencing rapid growth, accompanied by increasing interest in accounting for their environmental impacts. However, current approaches remain largely focused on relative impact reductions (energy efficiency, resource optimization), without ensuring compatibility with planetary boundaries. In this context, the concept of absolute sustainability emerges as an essential framework for guiding future developments of electronic systems.
This PhD thesis addresses several major scientific challenges: how can carrying capacities and sharing principles (core concepts of absolute sustainability) be identified for the electronics sector and consistently translated down to the levels of digital systems and integrated circuits? How can planetary boundaries be concretely integrated into the design of systems and circuits?
The main objective of the thesis is to move from a logic of relative environmental impact reduction toward designs that are compatible with planetary boundaries. It aims to define socio-technical scenarios to identify sharing principles, to conduct the first absolute life cycle assessment of a digital system, and to propose the first design of a circuit based on absolute limits, paving the way for sustainable development in electronics.
Controlling the composition and microstructure to achieve high magnetic performance in 1–12 rare earth-poor magnets
Permanent magnets based on rare earth elements (REEs), particularly neodymium-iron-boron (Nd-Fe-B) magnets, are strategically important for the development of more efficient motors and generators (electric vehicles, wind turbines). However, REEs, particularly Nd, are critical materials, with a high risk of supply disruption in the coming years. The growing demand for high-performance magnets requires the development of new types of magnets with reduce RE content. Iron-rich compounds, such as Sm-Fe12 (commonly known as phase 1-12), have very interesting intrinsic magnetic properties and are considered the best alternative to NdFeB magnets, allowing for a TR saving of around 35% by weight. However, achieving sufficient magnetic performance (remanence > 1 T and coercivity > 800 kA/m) depends on obtaining a suitable microstructure and remains the main challenge in the development of Sm-Fe12 magnets.
The aim of the thesis is therefore to improve the magnetic performance of this new family of magnets, in particular by controlling the composition and distribution of phases at grain boundaries. The doctoral work will combine an advanced experimental approach (development of Sm-Fe12 alloys, characterization of equilibrium phases, magnet manufacturing, magnetic characterization) with knowledge of phase diagrams to define compositions and optimal manufacturing conditions to achieve the targeted magnetic performances.
Model-Driven DevOps for Cloud Orchestration : Bridging Design-Time and Runtime Guarantees
Model-Driven Engineering (MDE) has traditionally relied on a clear separation between design and runtime, but this boundary no longer holds in today's cloud-native and edge environments, where infrastructures are heterogeneous, dynamic, and continuously evolving. Assumptions validated at design time may become invalid during execution, and modern orchestration platforms such as Kubernetes or OpenStack, while effective, remain weakly connected to architectural modeling environments. This results in a structural gap between architectural specification and actual operational behavior. To bridge this gap, this thesis proposes to develop a formal modeling framework for placement constraints across heterogeneous orchestration platforms, ensuring continuity between design-time validation and runtime guarantees. This framework would elevate placement constraints — resource locality, affinity, network latency, security isolation, and quality-of-service objectives — to first-class modeling constructs. At design time, it would enable static feasibility analysis and automated generation of deployment artifacts; at runtime, it would ensure continuous compliance monitoring and adaptive reconfiguration in response to violations. Expected contributions include a formal modeling language, bidirectional transformations between design-time models and runtime representations, and integration with Papyrus-based tooling. The ultimate goal is to ensure that architectural intent remains consistent and verifiable throughout the entire system lifecycle, from initial design through to production operation.
Non-invasively exploring the cerebellum microstructure with magnetic resonance
To better diagnose and monitor brain diseases, we need “non-invasive biopsies” to access the tissue cell-type composition and state without opening the skull. Magnetic resonance imaging (MRI) research efforts attempt to tackle the challenge but often lack cellular specificity because of the ubiquitous nature of water. Diffusion-weighted magnetic resonance imaging (dMRS) measures diffusion of intracellular and partly cell specific molecules in a region of interest, and forms a solid basis for resolving cell-types non-invasively. Among challenges, resolving signal contributions from the different cerebellar neurons could help monitor and understand neurodevelopmental and ataxic disorders. The cerebellum is a brain region representing 10% of the brain volume but containing more than half of the brain neurons, with the very large and complex Purkinje cells and the very small and round granule cells, both having very different functions and metabolism. The PhD project aims to disentangle these cells with complementary strategies: a classical dMRS approach and a quantum dMRS approach confronted to the state-of-the-art microstructure MRI methods.