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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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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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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 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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, 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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philosophy (PhD) Formation recommandée PhD in engineering or applied mathematics, especially in the field of numerical simulation Possibilité de poursuite en thèse Non Demandeur Disponibilité du poste
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significantly advanced grasp synthesis in simulation, deploying these models onto physical hardware remains a critical bottleneck. End-to-end learning methods often require prohibitively large datasets
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weight matrix by a product of two thin factors). Models are increasingly trained or fine-tuned so that they compress well, for instance by pushing their weights toward low rank. These methods are