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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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; 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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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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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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. Within the Institute—specifically within the Department of Ambient Intelligence and Interactive Systems (DIASI)—the Vision for Modeling and Localization Laboratory (LVML) conducts research in computer
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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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!), used in future quantum computer or spatial radio astronomy applications This internship provides an opportunity to improve our understanding of reliability physics at cryogenic temperature, and to offer
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. Moyens / Méthodes / Logiciels Python, Matlab, SPECTRA Profil du candidat Vous préparez un Bac+5 (Diplôme École d'Ingénieurs, master 2 ou équivalents) en computer vision, data science, IA, traitement du
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