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feeds Stage - Optimisation de metasurfaces optiques sur µLED par deep learning 144 / 723 vacancy Détail de l'offre Description du poste Domaine Technologies micro et nano Contrat Stage Intitulé de
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feeds STAGE – Deep learning pour la simulation de scénarios électronucléaires H/F 173 / 753 vacancy Détail de l'offre Description du poste Domaine Mathématiques, information scientifique, logiciel
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Excellent knowledge and experience in programming Python Excellent knowledge and experience in machine learning Excellent ability for cooperative collaboration Very good command of written and spoken
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Your Job Develop a reinforcement learning (RL) controller for a liquid–liquid gravity settler, trained entirely offline in a simulated environment Use existing physics-informed neural network (PINN
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consumption on complex algorithmic or cognitive tasks. This project is part of the ELEVATE MSCA Doctoral Network (https://www.elevate-dn.eu/) and co-supervised by our partners at the university of
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feeds Stage - Bac+4/+5 - Deep Learning pour les équations d'état tabulées - H/F 21 / 723 vacancy Détail de l'offre Description du poste Domaine Mathématiques, information scientifique, logiciel
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Your Job In this thesis you will work in the European Space Agency (ESA)-funded project Fast-EO (Fostering Advances in Foundation Models via Unsupervised and Self-Supervised Learning for Downstream
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advances the progress of research. Your tasks involve: Developing a machine learning-based model to map satellite retrievals to ground based air pollutant concentrations Conducting error assessment on the
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Your Job Generating growth models of human neurodevelopment, using unsupervised and supervised learning approaches Optimising leaning models to predict clinical phenotypes and cognitive maturation
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Your Job In this thesis you will work in the European Space Agency (ESA)-funded project Fast-EO (Fostering Advances in Foundation Models via Unsupervised and Self-Supervised Learning for Downstream