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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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installations (laser fs or ns, XFEL, synchrotrons, high pulse power), and developing numerical simulations and analysis tools. In order to develop new models into ESTHER, the candidate will be interacting with
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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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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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équipe et bonne humeur ; Vous avez une première expérience dans une infrastructure IT distribuée et complexe de type HPC ou cloud computing (formation en alternance, expérience professionnelle) ; Vous
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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