Molecular design for organic glass scintillation
Organic scintillators have many crucial applications in the field of security, nuclear medicine and high energy physics. In recent years Organic Glass Scintillators (OGS) showed increase performances compared to traditional plastic scintillator or organic monocrystal. The aim of this project is to synthesize and characterize new class of molecules that could form amorphous solid. The amorphous phase (organic glass) will be promoted by innovative molecular design that will focus on branched molecules with high degrees of mobility or a lot of stable conformers to make it hard to crystalized. We also envision using fluorophores bearing highly bulky moieties to limit p-stacking interactions, thereby hindering regular stacking and promoting amorphous phase. These molecules will contain aromatics building blocks such as benzene, biphenyl, diphenyl oxazole and naphthalene which are necessary for the scintillation process. In this project we aim to develop two different families of potential OGS:
- Fluorophores connected through alkyl or ethylene oxide linkers of various lengths: A major challenge will lie in establishing efficient and scalable synthetic routes suitable for gram-scale production. The proposed approach includes cross-coupling reactions, selective hydrogenation steps, and other established organometallic techniques.
- Single-benzene fluorophores (SBFs): SBFs represent a recently emerging class of compounds with promising features for the development of OGS. Their small size, easily tunable structures, and potential for covalent assembly make them attractive building blocks. A part of this project will be dedicated to the design of molecular architectures incorporating these fluorophores and promoting the formation of amorphous phases.
Formal Explanations for Artificial Intelligence
The candidate will contribute to the PyRAT formal analyzer, developped in the lab. This state-of-the-art analyzer is both used as a research sandbox and as an industrial-grade tool. As such, the candidate will work at the boundary of academia and industry.
The candidate missions are the following:
- actively build, update and deliver a state of the art on formal verification, in particular formal verification of machine learning and formal explanations
- contribute to scientific and technical discussions on PyRAT's design and implementations, and pursue said implementations
- investigate and apply the uses of PyRAT for formal explanations
- contribute to funded projects, either national or international, both by institutional and industrial actors, in particular by helping writing deliverables on such projects
- contribute to publications and/or technical reports around PyRAT
- help the dissemination of PyRAT, in particular by contributing to tutorials, courses and presentations and presenting them at scientific and industrial venues
Tools and diagnostic methods for the reuse of electronic components
The Autonomy and Sensor Integration Laboratory (LAIC) at CEA-Leti has the primary mission of developing sensor systems for the digitalization of systems. The team's activities are at the interface of hardware (electronics, optronics, semiconductors), software (artificial intelligence, signal processing), and systems (electronic architecture, mechatronics, multiphysics modeling).
In a context of exponential growth in electronics and scarcity of resources, the reuse of electronic components from end-of-life systems represents a promising avenue to limit environmental impact and support the development of a circular economy. The objective of this project is to develop an advanced diagnostic methodology to assess the health status of electronic components, particularly power components, to reintegrate them into a less constraining second-life cycle.
The postdoctoral researcher will be tasked with developing a comprehensive approach to evaluate the reuse potential of electronic components, with the aim of reintroducing them into second-life cycles. This will include:
- Identifying relevant health indicators to monitor the performance evolution of components (e.g., MOSFETs, IGBTs, capacitors, etc.);
- Setting up test benches and sensors adapted to measure electrical, thermal, or mechanical parameters, with the goal of detecting signs of aging;
- Analyzing degradation modes through experimental tests and failure models;
- Developing algorithms for predicting the remaining useful life (RUL) adapted to different usage scenarios;
- Contributing to scientific publications, the valorization of results, and collaboration with project partners.
Analysis of Gas Effluents for More Eco-Responsible Plasma Etching Processes
Traditionally used fluorinated gases, such as CF4 and C4F8, exhibit extremely high Global Warming Potentials (GWPs), significantly contributing to climate change. To address these environmental challenges, the project aims to promote the use of alternative low-GWP gases in combination with efficient exhaust abatement systems at the reactor outlet, while maintaining high-performance plasma etching processes. In this context, the postdoctoral researcher will be responsible for the analysis and characterization of gaseous species in an industrial plasma etching reactor using mass spectrometry. These measurements will be compared with the gas effluent at the outlet of the pumping and abatement systems. The main objectives are (i) to quantify the Destruction Removal Efficiency (DRE) of high and low GWP fluorocarbon gases during plasma processing and within the pumping and abatement stages, and (ii) to identify and propose innovative, environmentally responsible alternatives to minimize the release of high-GWP gaseous effluents.
Diamond-based electrochemical sensors for monitoring water pollution in urban environments
This postdoctoral position is offered by CEA List as part of the European UrbaQuantum project ("A novel, Integrated Approach to Urban Water Quality Monitoring, Management and Valorisation"), part of the HORIZON-CL6-2024-ZEROPOLLUTION-02 call for projects. The main objective of this project is to develop, in response to climate change, sensors, models, and protocols for better management of the water cycle in urban environments.
At the Sensors and Instrumentation for Measurement Laboratory (LCIM)of CEA List the postdoctoral fellow will contribute to the development of electrochemical sensors based on synthetic diamond and associated measurement protocols for the detection of pollutants such as pharmaceuticals, heavy metals, PFAS, and pesticides. These sensors will be miniaturized and integrated into a microfluidic cell, in partnership with CEA-Leti, then tested under real-world field conditions.
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.
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.
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.