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-Performance Computing for Exascale" contributes to the design and development of numerical methods and software components that will equip future European Exascale and post-Exascale machines. This program is
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Antarctic contribution scenarios: design and implement specific experiments aimed at evaluating the 'high-end' of projections, i.e. the highest possible contributions to sea-level rise. This will notably
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the context of LH materials, we conduct time-resolved spectroscopy experiments - UV/VIS absorption and fluorescence. We will carry out these experiments in the frame of the DEVILISH ANR project (2024-2027
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of the chosen nanorod mixture. The overall aim is to design systems that spontaneously self-assemble into assemblies with a target (crystalline or quasicrystalline) symmetry within each layer. This project will
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well as decentralized machine learning algorithms for large-scale clouds with dynamique parameters. -- Conception of machine learning algorithmes for resource allocation -- Numerical experiments -- Drafting research