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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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algorithm-hardware co-design of adaptive Transformer architectures using ReRAM analog in-memory computing. Using PyTorch and in-house ReRAM characterization data, you will develop reference models and
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addition (arithmetic masking) are commonly used to perform this mixing. The encryption algorithm is then carried out using the encoded data. The application of masking to post-quantum algorithm
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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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[4] M. Josien and R. Prat, Parallel and bias-free RSA algorithm for maximal Poisson-sphere sampling, Computer Physics Communications Moyens / Méthodes / Logiciels RSA, Mérope, ExaNBody Profil du
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collaborative projects with a large number of academic and industrial partners. The CEA is established in ten centers spread throughout France Reference 2026-41991 Description de l'unité Within the Institute
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of engineering school or Master 2 Desired skills : A strong motivation to learn and contribute to research in artificial intelligence. In-depth knowledge of computer science and programming languages
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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