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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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arrays. Applicant Profile You are a final-year Master’s or engineering student specializing in electrical engineering, microelectronics, applied physics or computer science. You are comfortable with
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-based power electronics devices Subject The internship will deal with the development of AlN-based power devices. It will cover electrical measurements, modeling and TCAD and can constitute the
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. Work expected In this internship you will perform electrical characterization and modeling of 28nm FDSOI (Fully-Depleted Silicon-On-Insulator [3]) transistors in cryogenic environment, advancing
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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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Profil du candidat Currently enrolled in your final year of a Master's program (M2) or an Engineering School (including gap year students). Strong skills in computer vision and machine learning
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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