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feeds Post-doctorat - Interaction spin-réseau par simulations ab initio et Machine Learning - H/F 195 / 667 vacancy Détail de l'offre Description du poste Domaine Matériaux, physique du solide Contrat
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feeds Post-doctorat - Méthodes de machine learning pour la simulation rapide des ondes de choc 3D - H/F 172 / 753 vacancy Détail de l'offre Description du poste Domaine Sciences pour l'ingénieur
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surface integral equation defined at material interfaces and discretizing it using a Boundary Element Method (BEM). High-order (HO) discretization schemes accelerate approximation convergence and thus
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programming and applied mathematics, and interested in neural networks and their hardware implementation. Some experience in machine learning would be an asset. Prior knowledge of emerging memory
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-manual dexterous setup to reliably solve complex assembly tasks. The following works conducted at the lab will serve as a basis for the internship: https://cea-list.github.io/cotograspweb/ https://cea
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qualifications master student or equivalent (2nd/3rd engineering school year) in computer science and/or machine learning; some knowledge of the Python programming language; ability to work in a team
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de l'offre L'objectif du stage est d'utiliser les outils du machine learning pour construire un potentiel classique fidèle aux simulations numériques ab initio. La base de données ab initio est déjà
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PYTHON, Pytorch, CUDA, proprietary software Profil du candidat Profile : Students in their 4th or 5th year of studies (M1, M2 or gap year) Computer vision skills Machine learning skills (deep learning
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: Master's (M2) or engineering-school student in machine learning, applied mathematics or computer science Strong linear algebra; probability and optimization are a plus Solid Python and PyTorch Care in
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énergétiques de ces outils, d'élaborer un outil de calcul d'impact en fonction des paramètres d'entrée (taille de données, type de machine…) et enfin de développer des optimisations logicielles CPU et/ou