Co-Design of Ultra-Compact Integrated Converters Leveraging Solid-State Micro-Batteries
Improving the performance of DC-AC and AC-DC power conversion systems is key to reducing system weight, extending operational autonomy, and enhancing compactness. This PhD project aims to explore novel topologies of integrated power converters by leveraging an emerging component: solid-state micro-batteries.
The research will begin with a system-level study of two representative applications — one AC-DC and one DC-AC — to define the constraints and opportunities offered by solid-state batteries. The candidate will then identify and co-optimize the most suitable converter topology for ultra-low power operation (in the milliwatt range) in conjunction with a matrix of available micro-batteries. The work will culminate in the design, fabrication, and experimental validation of the proposed architecture.
Co-supervised by Dr. Gaël Pillonnet (CEA-Leti, France) and Prof. Patrick Mercier (University of California, USA), the candidate will join a cutting-edge research environment focused on advanced Power Management Integrated Circuits (PMICs). This project offers a strong application-oriented dimension, targeting the co-optimization of circuits and emerging micro-storage components within ultra-compact systems, such as micro-motor actuation and micro-powering from mains voltage.
Joining our team means contributing to the advancement of disruptive technologies with high potential impact in fields such as healthcare, defense, and predictive maintenance.
Modeling and Characterization of Glass-Based Positive Electrodes for Li-Ion and Na-Ion Batteries
Amorphous cathode materials for Li-ion batteries have regained interest thanks to their practical capacities, which can exceed those of conventional commercial oxide cathode materials. Despite somewhat lower cell voltages, it could lead to significant enhancements in energy density. Nevertheless, the known amorphous cathode materials still face serious challenges prevent them from practical application: i) High irreversible capacity, ii) Low electronic conductivity, iii) Limited cyclability, iv) Lack of understanding of the involved phenomena due to their amorphous state, v) Most of the glassy cathode compositions explored so far are based on toxic vanadium.
In order to gain a deeper understanding of the influence of transition metals, glass formers, and synthesis conditions on the electrochemical performance of the cathode material, a PhD thesis is proposed in collaboration with CEA (Marcoule and Grenoble) and the National University of Singapore. The study will aim to combine various simulation approaches and experimental techniques, such as machine learning to design even more efficient cathode materials, computational modeling coupled with advanced in situ/operando characterization methods, and finally the development and performance evaluation of the synthesized materials.
Study of corrosion mechanisms of ceramics in molten chlorides salts
CEA Valduc operates processes involving molten salts. These salts, based on chloride compounds, can exhibit corrosive properties, particularly in the presence of impurities that lead to oxygen contamination. This results in the degradation of materials used in these processes. The study proposed here aims to understand these degradation mechanisms in order to identify the materials that best meet the needs of CEA Valduc. Beyond the specific requirements of CEA Valduc, this study also fits more broadly within ongoing research efforts to understand and mitigate corrosion in high-temperature molten salts environments, a major technological challenge for advanced modular reactors (AMRs).
The proposed work aims to study and compare various refractory materials in contact with chloride salts. Oxide materials (MgO, Y2O3, Ta2O5) and carbides (TaC) will be investigated in contact with CaCl2, NaCl, and KCl salts. The solubility of these materials in different molten salt media will be measured. The ultimate goal is to evaluate the behavior of these materials under aggressive conditions and to understand the mechanisms of their degradation.
Several studies have highlighted the predominant role of the material microstructure in relation to chemical durability. Initial characterization of the materials will be carried out using the facilities of Institute Jean Lamour (SEM/TEM, XRD). A thermodynamic study using the FactSage software will also be performed to predict material behavior and possible chemical reactions. The core of the thesis will consist of corrosion tests. Solubility constants of these different materials in chloride salts will be measured, followed by an investigation of phenomena occurring at the salt/material interface on sintered samples. Literature underscores the crucial influence of oxygen content on the corrosive nature of molten salts. Precise control and in situ measurement of oxygen levels is therefore critical for this work. To this end, the PhD candidate will have access to CEA’s facilities that enable work under inert atmosphere and analytical electrochemical measurements. Post-corrosion elemental analyses (ICP-AES/MS, UV-Vis spectroscopy) of the salts will be combined with microstructural characterizations of the samples to propose corrosion mechanisms for each material.
All experiments will take place at the CEA Valduc site, with occasional travel required to the IJL facilities in Nancy.
Study of degradation mechanisms in silicon/perovskite tandem devices and correlation with operational behavior
Organic-inorganic hybrid perovskites have become one of the most promising photovoltaic technologies of the last decade, paving the way for the development of even more efficient solar panels at an affordable cost. A perovskite cell can be combined with a silicon cell to form a tandem cell with optimized light absorption. Today, this technology has achieved a record efficiency of 34.9%.
The CEA Tandem Solar Cells Laboratory (LCT) at the INES Institute is developing silicon/perovskite tandem solar cells. One of the main stumbling blocks to the spread of this technology is its stability over time. Indeed, the ionic nature of the perovskite absorber and various problems at the interfaces lead to degradation mechanisms that may or may not be reversible. These problems are closely linked to illumination, temperature and their variations (day/night and thermal cycles).
The LCT implements accelerated tests (continuous or cyclic illumination, thermal cycling, electrical bias) to understand degradation mechanisms according to cell architecture, and to predict behavior in real-life rooftop situations. This last aspect is crucial to guarantee the reliability of future commercial tandem panels, with lifetimes equivalent to those of today's silicon panels.
The candidate will produce his/her own devices according to the laboratory's state of the art. These cells may be encapsulated using the laboratory's reference process. Accelerated stability tests will be carried out with different climatic chambers at the LCT, including one capable of alternating day/night cycles at different temperatures. This latter chamber will be used to apply accelerated aging modes which, in recent studies, have demonstrated their ability to reproduce real outdoor behavior. In addition, the candidate will be able to modify the cell to integrate so-called “passivating” layers to improve stability, adapt the current of each sub-cell of the tandem structure, or analyze spectral effects on cell performance. Finally, the candidate will benefit from the LCT's expertise in terms of characterization (electrical measurements, photoluminescence, electron microscopy, XRD, etc.) as well as from the contribution of Grenoble's nanocharacterization platform with advanced characterization tools (XPS, cAFM, TOF-SIMS, etc.).
Anisotropic approaches in graph signal processing. Application to graph neural networks.
Signal processing on graphs is based on the properties of an elementary operator generally associated with a notion of random walk / diffusion process. One limitation of these approaches is that the operator is systematically isotropic, a property that is passed on to any notion of filtering based on it. In multi-dimensional signal processing (images, video, etc), on the other hand, non-isotropic filters (or even filters that only take one direction into account) are used extensively, which greatly increases the possibilities. These non-isotropic filters are, in particular, the basic element of convolutional neural networks, which would likely have poorer performance with isotropic filters alone (i.e. impulse response with circular/spherical symmetry). The isotropy of the filters is also currently considered to be a major obstacle to the expressiveness of convolutional neural networks on graphs, which could be overcome using non-isotropic signal processing constructions on graphs. In addition to homogeneous graphs, operators used for signal processing or neural networks on bipartite or more generally heterogeneous graphs also have this property of isotropy where the neighbours of a node are treated identically. Although this time there is no obvious link with classical approaches, the notion of anisotropic or directional operator also seems relevant here to differentiate processing according to the multiple facets that can contribute to a given relationship.
To approach the concept of directionality in graphs, we will rely on the fact that a graph can often be viewed as a discretization of a Riemannian manifold. We will also examine extensions to bipartite graphs, which share similarities with a relationship between two manifolds, as well as heterogeneous graphs composed of multiple relations. Applications to graph neural networks will be explored to investigate the flexibility gained through directionality.
Bridging the embedding gap between expressive specification and efficient verification of machine learning
Formal verification of neural networks is facing a double-faceted issue. The expressiveness of specifications (as in: compact and close to human understanding) is apparently clashing with their efficient translation to state-of-the-art prover, who only support a fragment of arithmetic without quantifiers.
This thesis will investigate "global" properties. Such class of properties describe generic behaviours of neural networks, beyond the level of local samples. Such properties are currently partially specified and most of them cannot be soundly derived into standard prover queries. Using the expressive power of the WhyML specification langage, this thesis will strive to propose a common encoding for global properties and investigate their efficient compilation to prover queries thanks to automated graph editing techniques.
This thesis will also investigate the comparison of provers performance, in particular drawing inspiration from portfolio approaches.
Long-term and non-invasive plant monitoring using MIR spectroscopy
The LCO (french acronym for Optical Sensors Laboratory) develops innovative Silicium integrated photonic components (optical sources, waveguides, photodetectors, etc), sensors, and eventually systems. From upstream technological research to industrial transfers, those sensors apply in various fields such as environment, health, and security.
One of the laboratory research topic is mid-infrared spectroscopy of dense samples, using a photothermal detection technology. As we got convincing results applying our sensors for monitoring humans physiological parameters, we now wish to adapt them to plants. First laboratory trials reveal encouraging results, but their interpretation is at this stage out of reach because of the complexity of the measure, and the case study itself. Tackling this problematic is the thesis objective.
To achieve it, the candidate will establish an experimental program with the help of instrumentation and plant biology specialists. He will have access to the laboratory computational and experimental resources, as well as the CEA-Grenoble prototyping capabilities.
Study of the corrosion behaviour of complex multi-element materials/coatings in H2SO4 and HNO3 environments
This thesis is part of the CROCUS (miCro laboRatory fOr antiCorrosion solUtion design) project. The aim of this project is to develop a micro-laboratory for in situ corrosion analysis that can be brought into line with processes for synthesising anti-corrosion materials or coatings
By testing a wide range of alloy compositions using AESEC (a technique providing access to elementally resolved electrochemistry), the project will provide a real opportunity to build up a corrosion database in different corrosive environments, whether natural or industrial, with varying compositions, concentrations, pH and temperatures.
The aim of the thesis will be to study the corrosion behaviour of promising multi-element complex materials/coatings using electrochemical techniques coupled with AESEC.
The first part of this work concerns the determination of the limits of use of these promising alloys as a function of the proton concentration in H2SO4 and HNO3 media for temperatures ranging from room temperature to 80°C. The passivity of these alloys as a function of acid concentration will be studied using electrochemical techniques (voltammetry, impedance, AESEC).
The presence of certain minor elements in the composition of these alloys, such as molybdenum, may have a beneficial effect on corrosion behaviour. To this end, the passivation mechanisms involved will be studied using model materials (Ni-Cr-Mo), electrochemical techniques (cyclic and/or linear voltammetry, impedance spectroscopy and AESEC) and surface analysis.
The second part deals with the transition between passivity and transpassivity, and in particular the occurrence or non-occurrence of intergranular corrosion (IGC) as a function of oxidising conditions (presence of oxidising ions). The aim will be to determine the different kinetics (comparison between grain and grain boundary corrosion rates), as well as to validate the models set up to study IGC in steels.
Finally, the student will participate in the development of a materials database for corrosion in aggressive environments, whether natural or industrial, with different compositions, concentrations, pH and temperatures, enabling the development of new generations of corrosion-resistant materials or coatings through the use of digital design and artificial intelligence optimisation tools.
GenPhi : 3D Generative AI conditioned by geometry, structure and physics
The aim of this thesis is to design new 3D model generators based on Generative Artificial Intelligence (GenAI), capable of producing faithful, coherent and physically viable shapes. While 3D generation has become essential in many fields, current automatic generation approaches suffer from limitations in terms of respecting geometric, structural and physical constraints. The goal is to develop methods for integrating constraints related to geometry, topology, internal structure and physical laws, both stationary (equilibrium, statics) and dynamic (kinematics, deformation), right from the generation stage. The study will combine geometric perception, semantic enrichment and physical simulation approaches to produce robust, realistic 3D models that can be directly exploited without human intervention.
Robust and Secure Federated Learning
Federated Learning (FL) allows multiple clients to collaboratively train a global model without sharing their raw data. While this decentralized setup is appealing for privacy-sensitive domains like healthcare and finance, it is not inherently secure: model updates can leak private information, and malicious clients can corrupt training.
To tackle these challenges, two main strategies are used: Secure Aggregation, which protects privacy by hiding individual updates, and Robust Aggregation, which filters out malicious updates. However, these goals can conflict—privacy mechanisms may obscure signs of malicious behavior, and robustness methods may violate privacy.
Moreover, most research focuses on model-level attacks, neglecting protocol-level threats like message delays or dropped updates, which are common in real-world, asynchronous networks.
This thesis aims to explore the privacy–robustness trade-off in FL, identify feasible security models, and design practical, secure, and robust protocols. Both theoretical analysis and prototype implementation will be conducted, leveraging tools like Secure Multi-Party Computation, cryptographic techniques, and differential privacy.