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feeds Scaling analog in-memory computing towards large generative AI models H/F 192 / 778 vacancy Vacancy details Position description Category Micro and nano technologies Contract Internship Job title
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; PyTorch; parallel and GPU computing Applicant Profile The internship is intended for a final-year Master’s student or engineering student specialising in applied mathematics, signal processing or
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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 - HED models development into the ESTHER code and experimental applications - H/F 187 / 668 vacancy Détail de l'offre Description du poste Domaine Physique du noyau, atome, molécule
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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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scientifique, logiciel Contrat Post-doctorat Intitulé de l'offre Using generative AI to simulate chemically disordered nuclear materials at the atomic level H/F Sujet de stage This postdoc aims to enhance
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réduire les performances de lecture et de manipulation des qubits. Intégré à un laboratoire de recherche en conception intégrée, et en particulier au sein de l’équipe quantum computing, vous aurez
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library for computing the elementary matrices associated with near, singular and quasi-singular interactions using advanced integration methods from the literature [2–6], or variants to be defined as
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Computing Devices at CEA-Leti in Grenoble, you will contribute to the development of Bayesian learning methods suited to resistive memories (ReRAM). You will build on the team’s previous work, which
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and sufficient. Assigning meaning to prototypes is however a time-consuming task, as the various models tend to learn prototypes that do not carry sufficiently high-level meaning. A parallel line of