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. A second key step is to obtain high quality AlN-based drift layers. A third step will be to passivate the layers which we are developing in collaboration with CNRS-LTM. The goal of the internship
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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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7265, CEA- CNRS-l'Université d'Aix-Marseille Dans un contexte marqué par la surexploitation des ressources naturelles, la pollution issue des activités industrielles et la croissance démographique, la
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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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Scaling analog in-memory computing towards large generative AI models H/F Subject Your work will span device-to-algorithm co-design, focusing on state-of-the-art small language model engineering and
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stripping - De participer à des étapes engineering - De tracer l’activité sur le logiciel MES du CEA-LETI. - De rendre compte des résultats de vos travaux aux différentes parties prenantes
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École d'ingénieurs Recommended training Master of Science, Master of Engineering, Master of Research PhD opportunity Non General information Organisation The French Alternative Energies and Atomic Energy
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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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: 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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philosophy (PhD) Formation recommandée PhD in engineering or applied mathematics, especially in the field of numerical simulation Possibilité de poursuite en thèse Non Demandeur Disponibilité du poste