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Requirements Research FieldEngineeringEducation LevelMaster Degree or equivalent Skills/Qualifications Proficiency in English and French is required. In addition, international experience will be a decisive
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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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macroscopic designs for soft-tissue healing. The project will be conducted using in vitro models of bacterial adhesion and fibroblast proliferation in collaboration with University of Gothenburg and in vitro
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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
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–pollutant interactions, organoids-on-a-chip, and decellularized ECM (dECM), while LMCD specializes in biocompatible hydrogel design and characterization. Novel hydrogels will be developed from dECM, modified
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used to develop networks capable of self-learning and self-optimisation, adapting to real-time changes in traffic and demand. The successful candidate will contribute to designing solutions that optimise
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Wikibase instance Curate and model historical migration datasets within the dedicated Wikibase instance Contribute to the design of ontologies and metadata schemas for the knowledge graph Develop data-driven