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with the startup Nellow, with the goal of advancing FESO-based logic-in-memory architectures toward industrial deployment. Where to apply Website https://emploi.cnrs.fr/Offres/CDD/UMR137-MANBIB-028
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implement and train neural network architectures, including Physics-Informed Neural Networks (PINNs), in order to integrate physical constraints into the learning process and improve the identification and
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. The work will be primarily computational, focusing on the development of deep neural network model architectures and their training. It will involve extending the preliminary results we have already obtained
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• Exploration of dielectric-based resonant and non-resonant nanophotonic architectures compatible with large-area fabrication • Demonstration of prototype metasurface devices combining optical selectivity
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extensive experience in the development and characterization of new renewable macromolecular architectures. The candidate (M/F) will carry out their work as part of a European project involving a dozen
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• Exploration of dielectric-based resonant and non-resonant nanophotonic architectures compatible with large-area fabrication • Demonstration of prototype metasurface devices combining optical selectivity
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concerned the conversion of (thio)esters into (thio)ethers via nickel-catalyzed intramolecular decarbonylation. This research axis was explored through a PhD project carried out in the laboratory and has
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systems. NumPEx aims to support the Computational Scientific and Engineering (CSE) community in leveraging the capabilities and potential of these new architectures through expanded and reusable exascale
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architecture, coat assembly, and organelle identity. By investigating how cells build and remodel carriers and compartments around oversized cargo, the project seeks to reveal general principles of secretory