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organelles, and macro-complex structures in their cellular context (in situ structural biology). The laboratory Cell and Plant Physiology (LPCV), which aims to understand the adaptive response of microalgae
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an innovative technology through the complete development process: Physical modelling → CFD simulation → prototype development → experimental validation → scientific publications and technological innovation
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project is to achieve the integration of quantum devices within Fully Depleted Silicon-On-Insulator (FD-SOI) technology on a 300 mm platform. The success of this integration critically depends
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, numerical systems designs. The project builds on complementary developments from both teams: TRAITOR for experimental fault injection and µArchiFI for formal securi Applicants should hold a PhD in computer science
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Physics, Applied Mathematics, or Computer Science, with experience in deep generative models (VAE, diffusion) and Python/PyTorch. Applications from statistical physicists are welcome. STILL HESITATING? The
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, Physical Chemistry, or Electrochemistry. - Knowledge of rechargeable batteries and a solid understanding of the associated scientific and technological challenges are essential. - Knowledge of Organic
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tutorials as well as in the valorization of our innovations. #CEA-List ; #researcher ; #Nanotechnology ; #Simulation Model You have an engineering/master's degree or a PhD in the field of computer science
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physics, microwave engineering, electrical engineering, physics or a closely related field. The ideal candidate will have: A solid background in plasma physics and/or microwave diagnostics; Experience in
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Numerical analysis of fully explicit phase-field models for dynamic fracture of materials H/F Numerical analysis of fully explicit phase-field models for dynamic fracture of materials Understanding material fracture is paramount in the nuclear industry to ensure the structural integrity and...
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The detection of out-of-distribution (OoD) samples is crucial for deploying deep learning (DL) models in real-world scenarios. OoD samples pose a challenge to DL models as they are not represented in the training data and can naturally arrive during deployment (i.e., a distribution shift),...