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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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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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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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suggest that clock glitches induce sampling faults on sensitive sequential elements, but the corresponding fault models remain to be formally validated. In parallel, CEA-List has developed µArchiFI [3-4
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combining machine learning and large-scale atomistic simulations on high-performance computing (HPC) systems. It offers the chance to work on a cutting-edge project, with potential to continue into a
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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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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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applications in robotics, augmented reality, archaeology, and industrial inspection. Modern methods, such as Neural Fields (NeRF, Instant NGP) and Gaussian Splatting, enable the creation of highly