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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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; PyTorch; parallel and GPU computing Applicant Profile The internship is intended for a final-year Master’s student or engineering student specialising in applied mathematics, signal processing or
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future distributed communication networks, for example to provide localisation and detection functionalities, or to proactively adapt network/radio resources to actual local needs. Contract duration
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l’interférométrie laser sur une fibre optique (système DAS, distributed acoustic sensor) permet de mesurer la déformation axiale (utilisation de l’élongation de la fibre et mesure du temps de parcours du pulse
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Intership Agentic Multimodal AI for Understanding and Reasoning over Complex Technical Documents-H/F
develop multimodal agentic AI for understanding and reasoning over complex technical documents containing text, tables, figures, diagrams, and equations distributed across multiple pages. The work will
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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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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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emerging applications requires low–temperature operation of CMOS transistors, such as spatial applications, high performance computing, high energy physics…, with a temperature of operation below 100K
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réduire les performances de lecture et de manipulation des qubits. Intégré à un laboratoire de recherche en conception intégrée, et en particulier au sein de l’équipe quantum computing, vous aurez