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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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for reproducible, efficient and scalable training and inference on parallel, distributed and GPU-accelerated computing systems Benchmark the developed approaches against established methods, assessing
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for interdisciplinary research at the interface of energy systems, high-performance computing, and quantum computing Strong background in mathematics, optimization, numerical methods, or scientific
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two companies. The project has partners from eight different EU countries. All 15 Ph.D. projects are within the overall theme of neuromorphic computing and analog signal processing, targeting
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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
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embedded FPGA-based intelligence near seismic sensor arrays, you will explore methods to maximize prediction performance while minimizing data transmission and latency. Your key tasks include: Evaluating
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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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exactly solvable models) alongside large-scale numerical simulations on high-performance computing facilities Characterize the transitions that govern quantum information protection, from encoding and
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collaboration of the Air Quality and Emission Optimization group at the Institute of Climate and Energy systems (ICE-3) at Forschungszentrum Jülich and the group working on Methods for Model-based Development at