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, laboratory meetings, as well as at workshops and conferences covering the various aspects of the thesis: high-performance computing, numerical methods, and computational cosmology. An annual meeting of
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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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Your Job Design, fabricate, and characterize advanced FDSOI devices for neuromorphic computing applications Develop and optimize semiconductor fabrication processes in a state-of-the-art cleanroom
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Inria, the French national research institute for the digital sciences | Grenoble, Rhône-Alpes | France | 18 days ago
the supervision of Adrien Taylor and Scott Pesme. The successful candidate will implement and compare different parallel and stochastic numerical optimization paradigms accurately study the selected
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-machine learning approach based on the following key steps. It is expected that such combination will improve computational efficiency compared to traditional numerical methods maintaining high level
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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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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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computing Experience in the development and application of process-based models and/or model–data fusion and data assimilation methods Very good programming skills in Python and/or Fortran; knowledge of
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) contribute to the research tasks, using both analytical and numerical methods, contributing to numerical validation of analytical predictions where relevant; (b) write up results for publication and
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study, through numerical simulations, the transport of ions and water in sub-nanometer hexagonal boron nitride (hBN) channels using an ML-MD approach with DFT-accurate MACE potentials. A central