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Offer Description Funding: 36 months, CIFRE (https://www.anrt.asso.fr/fr/le-dispositif-cifre-7844 ) Starting date: November / December 2025 Keywords: Physically informed machine learning, Industrial
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process structures. However, this method is limited by the inductive bias of the predefined superstructure. Innovation: Increased computing power and advances in data science have popularized new algorithms
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problems of on-device learning for spintronic devices, proposing and impl menting technical solutions and communicating his scientific results Where to apply E-mail job-ref-waft5vlowa@emploi.beetween.com
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European project (IsoPROPEL) in close collaboration with Forschungszentrum Jülich (Germany) for cell design and catalytic testing and ITQ Valencia (Spain) for the synthesis of catalysts. The overall goal
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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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be highly interdisciplinary. Two different profiles are possible for this position: either a profile in engineering sciences or biomedical physics, with a strong desire to learn about microbiology
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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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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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–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