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Job related to staff position within a Research Infrastructure? No Offer Description BRIEF This project is part of the ANR RéCLasSIF initiative, which brings together a network of campuses under
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Doctoral Network aiming to train 15 PhD researchers in the efficient and clean use of renewable synthetic fuels. Candidates will develop advanced skills in combustion science, chemical kinetics, and digital
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(Decarbonising Energy-intenSIve industries with REnewable synthetic fuels) is a Marie Skłodowska-Curie (MSCA) Doctoral Network, designed to train the next generation of specialists in e-fuel utilization. DESIRE
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renewable electricity and sustainable feedstocks, represent a promising solution, enabling deep decarbonization. DESIRE is a Marie Sklodowska-Curie Doctoral Network aiming to train 15 PhD researchers in
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group investigates the cellular and molecular mechanisms wiring neuronal networks. The candidate will join a project aiming at understanding how migrating neurons, developing axons and endothelial cells
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The DESIRE project (Decarbonising Energy-intenSIve industries with REnewable synthetic fuels) is a Marie Skłodowska-Curie (MSCA) Doctoral Network, designed to train the next generation of specialists in e-fuel
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global anthropogenic CO2 emissions. Decarbonizing the cement industry to achieve net-zero is particularly challenging because a significant fraction of CO2 emissions stems from the intrinsic chemical
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Inria, the French national research institute for the digital sciences | Villeurbanne, Rhone Alpes | France | about 1 month ago
, 3], significant challenges related to running complex AI algorithms such as Deep Neural Network (DNN) inference on lightweight platforms with limited computational power and relying on potentially
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obtained from multi-sensor networks across urban areas. A novel synergy approach will be developed that combines observations of thermal stratification from microwave radiometers, profiles of wind and
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networks, ensemble algorithms, and other advanced architectures, the objective will be to accurately predict the state of health (SoH) of batteries in the short, medium, and long term, including under