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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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, parallel research axes: pushing model accuracy on systems of several thousand atoms with an IWAE architecture; equipping it with the ability to quantify its own uncertainty — a prerequisite for any use
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. To achieve this, it draws upon its scientific expertise across a wide range of high-tech fields, including artificial intelligence, software design, simulation, cybersecurity, and quantum computing
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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
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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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afin de repousser les limites actuelles. Durée du contrat (en mois) 6 mois Description de l'offre Rejoignez-nous en Stage ! CEA Tech Corporate from CEA Tech on Vimeo . En tant que stagiaire au CEA
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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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l'optimisation du greffage de nanoparticules d'or sur fibre optique pour la détection biologique Durée du contrat (en mois) 6 mois Description de l'offre Rejoignez-nous en Stage ! CEA Tech Corporate from