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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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will initially focus on algorithm development and simulation. Starting from reference methods, you will evaluate different strategies and progressively incorporate realistic ReRAM device characteristics
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will involve: - Understanding the various sensors. - Implementing a calibration algorithm using AI-based methods. - Adopting a rigorous scientific approach to validate the relevance of the proposed
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, “Physics-Informed Super-Resolved Ultrasonic Imaging,” 11th Junior Conference on Data Science and Engineering, 2026 [ Online ]. # Cea-List Methods / Means FMC ultrasonic data; Algorithm unrolling
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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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the understanding of reliability physics, and optimizing their potential down to deep cryogenic temperature. [1] https://irtnanoelec.fr/programme/q-loop-preparer-le-controle-commande-de-lordinateur
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preparatory work to the PhD that will follow. Contract duration (months) 4 to 6 months Job description Aluminum nitride (AlN) is an UWBG (ultra-wide bandgap) semiconductor that could potentially be used to
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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
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, publication) is expected. Practical information. The candidate must have obtained a PhD thesis within the last three years and have solid experience in scientific computing and its implementation. Expertise
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the price to propose and test an improvement. The internship may lead to a PhD starting in October 2027. # Cea List Moyens / Méthodes / Logiciels Pyyhon - PyTorch Profil du candidat Profile