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dynamic hand control — that enables a bi-manual dexterous setup to reliably solve complex assembly tasks. Contract duration (months) 6 mois Job description As an intern at the CEA, you will have the
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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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Congress of Embedded Real Time Systems (ERTS 2026) , 2026. ⟨10.82331/ERTS.2026.42⟩. ⟨hal-05513843⟩ [2] Eclipse Aidge. https://eclipse.dev/aidge/ [3] Projet DeepGreen - https://deepgreen.ai/ [4] Projet
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dans les outils d'ACV et d'analyse de coûts du Cea-LITEN. Références: [1] https://doi.org/10.1016/j.jclepro.2024.144298 [2] https://doi.org/10.1016/j.resconrec.2024.107526" Profil du candidat Diplôme
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l’ explicabilité des modèles. Pour aller plus loin : https://www.itg.be/fr/research/projets/systeme-dhemoculture-simplifie-simble https://doi.org/10.1155/2024/6465280 Profil du candidat Étudiant(e
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connaitre - M.Faure 2026: https://ieeexplore.ieee.org/document/11561453 - M. Alsukour EUMW2025: https://ieeexplore.ieee.org/document/11234388 Profil du candidat Qu’attendons-nous de vous ? Vous
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BI Outils CEA : SAP / SPI / WEBI / SAPHIR / GeOSOL / FEBIA / IRIS Votre réactivité, esprit d'initiative, rigueur, sens relationnel ainsi que votre esprit d'analyse sont des atouts pour ce poste
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Internship Job title Internship in AI: Graph-based Artificial Neural Networks H/F - Grenoble Subject The internship is focus on evaluation of Graph Neural Network coupled with radar sensor data. The fisrt
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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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-V processor. For near-sensor data processing, we use transformer-based artificial intelligence (AI) algorithms. These models significantly improve performance but require high computational power. To