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Weebit Nano. Applicant Profile M2 student in Electrical Engineering, Computer Science, Applied Mathematics, Data Science, Computational Neuroscience or related fields, with strong quantitative skills
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Sécurité Matérielle, CEA-LETI, 07 86 86 15 21, [email protected] [1] https://spectreattack.com/spectre.pdf [2] https://meltdownattack.com/meltdown.pdf [3] https://openhwfoundation.org/ [4
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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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conditions de fragmentation). [1] Rutz,A. et al. (2026) MultiMS2: A curated multi-modal, multi-energy spectral library for metabolomics. Gigascience. (https://doi.org/10.1093/gigascience/giag069). [2
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avec le serveur HTTP. Ces serveurs suivent pour la plupart la même architecture et utilisent des outils similaires, tels que Kerberos[1], Celery[2], Apache[3] ou Flask[4]. [1] https
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l’informatique, au traitement d’images, à l’intelligence artificielle ou à la science des données (data science). - Connaissances en Machine Learning et en traitement d’images (OpenCV, bibliothèques de
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quantification ainsi que sur le framework de deep learning, développé au LIAE, pour le profiling, la compression et l’export sur cible matérielle (Aidge[3]). [1] https://le-wm.github.io/ [2] https
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d’Aidge (framework de deep learning développé au LIAE), leurs évaluations et leurs validations sur cible. Quelques références: - Cross-Architecture KD: https://openaccess.thecvf.com/content/ACCV2022
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: Master's (M2) or engineering-school student in machine learning, applied mathematics or computer science Strong linear algebra; probability and optimization are a plus Solid Python and PyTorch Care in
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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