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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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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
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and sufficient. Assigning meaning to prototypes is however a time-consuming task, as the various models tend to learn prototypes that do not carry sufficiently high-level meaning. A parallel line of
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. B 106, L161110 (2022). [2] A. Castellano, R. Béjaud, P. Richard, O. Nadeau, C. Duval, G. Geneste, G. Antonius, J. Bouchet, A. Levitt, G. Stoltz, et F. Bottin, Machine learning assisted canonical
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, parallel research axes: pushing model accuracy on systems of several thousand atoms with an IWAE architecture; equipping it with the ability to quantify its own uncertainty — a prerequisite for any use
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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
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library for computing the elementary matrices associated with near, singular and quasi-singular interactions using advanced integration methods from the literature [2–6], or variants to be defined as
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and material savings). One important first step is to achieve ohmic contacts on n- and p-type regions, which can be achieved through electrostatic doping developed with our partner, Stanford University
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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