Design and development of a modular high-side test bench for application validation of Grand Gap components
Wide bandgap transistors (GaN, SiC) play a key role in power electronics, but their industrial integration remains hampered by implementation difficulties. The high-side component, within a bridge arm structure, is particularly sensitive to voltage and current transients, which are highly dependent on routing, topology, and switching modes (ZVS, ZCS). Its floating nature makes measurements complex and can disrupt switching during application testing. A methodology adapted to fast transients was developed during a thesis, resulting in a patented test bench for characterizing low-side components. The subject of the postdoctoral research presented here aims to adapt this methodology to high-side components, which are more complex to drive and measure, in order to characterize and model aging due to gate transients under realistic conditions. The test bench will enable the generation of reproducible stress profiles on low-side and high-side components, and the precise measurement of key parameters such as threshold voltage and dynamic instabilities. To achieve these objectives, a new bench will be designed, incorporating specific control and measurement systems, with a view to application testing and targeted aging tests.
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Secure Implementations of Code-Based Post-Quantum Cryptography: Software-Hardware Co-Design and Side-Channel Resistance
Quantum computing threatens traditional cryptographic schemes like RSA and ECC, prompting the need for post-quantum cryptography (PQC). NIST’s standardization process selected algorithms like HQC, a code-based Key Encapsulation Mechanism. Efficient and secure implementation of these algorithms, especially in resource-constrained environments such as IoT and embedded systems, remains a challenge. Physical attacks, particularly side-channel and fault injection attacks, require robust countermeasures like masking, shuffling, and hiding. These protections, however, introduce performance overhead, making hardware/software co-design essential. The project focuses on the secure software implementation of HQC with strong resistance to physical attacks. Target platforms include RISC-V embedded systems. The research involves designing and evaluating side-channel countermeasures on these platforms. Later phases will extend the work to FPGA prototypes for validating security in hardware. ASIC design may follow to optimize area, power, and performance while maintaining security. The candidate will also develop algorithmic and architectural techniques for attack mitigation. Contributions will include open-source tools and benchmarking. The work will support secure deployment of PQC in real-world applications.
PeRovskItes based SpectroMetric imagers (PRISM)
For many years, CEA has been involved in the development of semiconductor-based X-ray and gamma-ray spectrometric imagers (20keV-1MeV). The main applications targeted are medical imaging, nuclear, security and astronomical observations. State of the art detectors based on Cadmium Telluride single crystals (Cd(Zn)Te) are efficient but costly and their detection area is limited. In recent years, halide perovskites have proven to be promising alternatives. The energy resolution of single pixel detectors made with these crystals proved to be in line with that of CdTe based detectors. However, to this day, the performances of perovskite single crystals as application specific spectroscopic imagers have never been established. The objective of this 18-months project (PRISM) is to benchmark halide perovskite single crystals from different suppliers for X and gamma-ray spectro-imaging in key areas for CEA: astrophysics
Multi-Agent Negotiation for Collaborative Resource Placement in Distributed Cloud Networks
This research project aims to design a decentralized and autonomous resource management system for heterogeneous cloud networks. Building on the shift toward distributed architectures driven by concerns over data sovereignty and performance, the project seeks to move beyond the traditional centralized control plane model used in Kubernetes. Each organization involved in a federation of clusters would be represented by an intelligent agent, capable of negotiating resource placement according to its own objectives while preserving data confidentiality. The interaction among these agents is modeled as a multi-agent game, where incentive mechanisms are designed to reach a mutually beneficial equilibrium. The project plans to formalize the problem, adapt multi-agent reinforcement learning methods to the challenges of distributed settings (such as fault tolerance and asynchronous communication), and develop a functional implementation in Rust. In doing so, it lays the groundwork for a new paradigm of collaboration among cloud service providers.
Modeling and integrating Local-First Data Types
Existing modeling frameworks have limited collaboration capabilities. Collaboration at model level is one of the top desired features as identified in the literature. However, most port of solutions primarily rely on cloud-based and centralized databases as their technological solution. While these solutions ease collaboration among connected partners by employing concurrency control techniques or adopting a "last writer wins" policy, they do not support disconnected collaboration scenarios, which is an important feature for designing local-first sofware. This situation presents a significant compromise: utilizing cloud-based solutions and sacrificing data ownership control versus adopting separate instances and without collaborative capabilities. The objective of this postdoctoral project is to contribute and extend an existing local-first Model-Based Systems Engineering (MBSE) framework, related to this work [5], built upon specialized Conflict-free Replicated Data Types (CRDTs). The goal is to enable real-time collaboration through modeling-specific CRDTs. The proposed approach involves extending a middleware layer utilizing CRDTs to seamlessly synchronize distributed, offline-capable engineering models.
DTCO for RF & mmW Applications:Focus on Homogeneous & Heterogeneous Chiplet Hybrid Bonding Challenge
In recent years, there have been numerous technological advancements in silicon-based semiconductors. However, the limits in terms of frequency performance and power seem to have been reached, requiring the development of new type III-V devices (such as InP and GaN) that are faster, more powerful and well adapted for new RF mmW applications. For reasons of flexibility, performance, and cost, it is crucial to co-integrate these new high-performance III-V components with the more traditional silicon technologies. This is one of the major objectives of the proposed topic.
The focus will be on the design and optimisation of millimetre-wave RF circuits using 3D heterogeneous hybrid bonding assembly technology. In recent years, numerous test vehicles have been fabricated and characterised to demonstrate the advantages and disadvantages of the hybrid bonding assembly process for millimetre wave RF applications. The aim is to extend this work and focus the studies and research on real RF systems, such as millimetre-wave power amplifiers. The DTCO (Design and Technology Co-Optimisations) approach will not only enable the design of efficient 3D RF circuits, but will also allow the adaptation of different 3D design rules to make 3D hybrid bonding technology relevant for the production of millimetre-scale 3D integrated systems.
High-performance computing using CMOS technology at cryogenic temperature
Advances in materials, transistor architectures, and lithography technologies have enabled exponential growth in the performance and energy efficiency of integrated circuits. New research directions, including operation at cryogenic temperatures, could lead to further progress. Cryogenic electronics, essential for manipulating qubits at very low temperatures, is rapidly developing. Processors operating at 4.2 K using 1.4 zJ per operation have been proposed, based on superconducting electronics. Another approach involves creating very fast sequential processors using specific technologies and low temperatures, reducing energy dissipation but requiring cooling. At low temperatures, the performance of advanced CMOS transistors increases, allowing operation at lower voltages and higher operating frequencies. This could improve the sequential efficiency of computers and simplify the parallelization of software code. However, materials and component architectures need to be rethought to maximize the benefits of low temperatures. The post-doctoral project aims to determine whether cryogenic temperatures offer sufficient performance gains for CMOS or should be viewed as a catalyst for new high-performance computing technologies. The goal is particularly to assess the increase in processing speed with conventional silicon components at low temperatures, integrating measurements and simulations.
Design and Implementation of a Neural Network for Thermo-Mechanical Simulation in Additive Manufacturing
The WAAM (Wire Arc Additive Manufacturing) process is a metal additive manufacturing method that allows for the production of large parts with a high deposition rate. However, this process results in highly stressed and deformed parts, making it complex to predict their geometric and mechanical characteristics. Thermomechanical modeling is crucial for predicting these deformations, but it requires significant computational resources and long calculation times. The NEUROWAAM project aims to develop a precise and fast thermomechanical numerical model using neural networks to predict the physical phenomena of the WAAM process. An internship in 2025 will provide a database through thermomechanical simulations using the CAST3M software. The post-doc's objective is to develop a neural network architecture capable of learning the relationship between the manufacturing configuration and the thermomechanical characteristics of the parts. Manufacturing tests on the CEA's PRISMA platform will be conducted to validate the model and prepare a feedback loop. The CEA List's Interactive Simulation Laboratory will contribute its expertise in accelerating simulations through neural networks and active learning to reduce training time.
Digital correction of the health status of an electrical network
Cable faults are generally detected when communication is interrupted, resulting in significant repair costs and downtime. Additionally, data integrity becomes a major concern due to the increased threats of attacks and intrusions on electrical networks, which can disrupt communication. Being able to distinguish between disruptions caused by the degradation of the physical layer of an electrical network and an ongoing attack on the energy network will help guide decision-making regarding corrective operations, particularly network reconfiguration and predictive maintenance, to ensure network resilience. This study proposes to investigate the relationship between incipient faults in cables and their impact on data integrity in the context of Power Line Communication (PLC). The work will be based on deploying instrumentation using electrical reflectometry, combining distributed sensors and AI algorithms for online diagnosis of incipient faults in electrical networks. In the presence of certain faults, advanced AI methods will be applied to correct the state of the health of the electrical network's physical layer, thereby ensuring its reliability.
Advanced reconstruction methods for cryo-electron tomography of biological samples
Cryo-electron tomography (CET) is a powerful technique for the 3D structural analysis of biological samples in their near-native state. CET has seen remarkable advances in instrumentation in the last decade but the classical weighted back-projection (WBP) remains by far the standard CET reconstruction method. Due to radiation damage and the limited tilt range within the microscope, WBP reconstructions suffer from low contrast and elongation artifacts, known as ‘missing wedge’ (MW) artifacts. Recently, there has been a revival of interest in iterative approaches to improve the quality and hence the interpretability of the CET data.
In this project, we propose to go beyond the state-of-the-art in CET by (1) applying curvelet- and shearlet-based compressed sensing (CS) algorithms, and (2) exploring deep learning (DL) strategies with the aim to denoise et correct for the MW artifacts. These approaches have the potential to improve the resolution of the CET reconstructions and facilitate the segmentation and sub-tomogram averaging tasks.
The candidate will conduct a comparative study of iterative algorithms used in life science, and CS and DL approaches optimized in this project for thin curved structures.