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Scaling analog in-memory computing towards large generative AI models H/F Subject Your work will span device-to-algorithm co-design, focusing on state-of-the-art small language model engineering and
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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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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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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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As part of its software suite provided to its clients, Cegid and its partners use cookies on this website to: Provide the basic functions of the website; Obtain audience insights; Allow users to use
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As part of its software suite provided to its clients, Cegid and its partners use cookies on this website to: Provide the basic functions of the website; Obtain audience insights; Allow users to use
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As part of its software suite provided to its clients, Cegid and its partners use cookies on this website to: Provide the basic functions of the website; Obtain audience insights; Allow users to use
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As part of its software suite provided to its clients, Cegid and its partners use cookies on this website to: Provide the basic functions of the website; Obtain audience insights; Allow users to use