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feeds Adaptive Learning for Transformers with ReRAM Analog In-Memory Computing 152 / 767 vacancy Vacancy details Position description Category Miscellaneous Contract Internship Job title Adaptive
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feeds Scaling analog in-memory computing towards large generative AI models H/F 192 / 778 vacancy Vacancy details Position description Category Micro and nano technologies Contract Internship Job title
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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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publication about this benchmark. # Cea List Moyens / Méthodes / Logiciels AI, Deep Neural Network, Computer Vision, Human behavior analysis Profil du candidat Profile - Students in their 4th or 5th year
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/Computer Vision pour analyser les images radioscopiques d'un mélange triphasique. Stage M2 de traitement et d'analyse d'images issues de radioscopies X de mélanges triphasiques. Mise en œuvre de méthodes de
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du service en vous appuyant sur votre compétence micro-fabrication, intégration & packaging microfluidique. Vous apporterez votre vision pour la conception des systèmes microfluidiques en
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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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computing circuit architecture Contract duration (months) 5 to 6 months Job description Context: As part of our ASIC design activities, we are developing an embedded computing platform based on a
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automatic and randomly queried baselines This internship offers an introduction to research in machine learning and computer vision. Depending on the results, it may lead to a scientific publication and may
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gaussian representation meets perception models for bev segmentation. In : 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, 2025. p. 2250-2259. [3] KERBL, Bernhard, KOPANAS