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of predicting electronic, structural, and thermal quantities while leveraging underlying symmetries for computational efficiency. There will be a significant computational component in deploying multi-GPU codes
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Zurich or another Swiss university Proven experience in software development Strong Python programming skills, with demonstrable experience in several of the following areas: GUI development CUDA for GPU
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multiphase flows Your tasks Develop and extend the in-house GPU-accelerated multiphase Lattice Boltzmann (LBM) code for DNS-grade boiling multiphase flow related to nuclear reactor operation, including bubble
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megawatts. To transfer energy efficiently from the grid to CPUs/GPUs, higher system voltages are required in data centres/computer racks, and efficient power electronics converter systems based on SSTs
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and clinical MR systems fully dedicated to research, state-of-the-art local and scalable cloud-based compute infrastructure (CPU, GPU) and workshops for mechanical, electrical and electronic development
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significant computational component in deploying multi-GPU codes to efficiently train on the large, densely-connected and graph-structured data encountered in our systems of interest. Your contributions would
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of computer graphics fundamentals, numerical methods, and GPU/parallel computing concepts. Experience with at least one major deep learning framework (PyTorch preferred). Excellent problem-solving skills and
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applications. HPC and orchestration of scientific data processing workflows. Parallel computing (GPU & CPU). good software engineering practices for scientific software (version control, testing, continuous
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, including running large-scale machine learning models (e.g., PyTorch, JAX) on GPUs in an HPC environment and maintaining reproducible workflows. Extensive prior experience developing pipelines and analytic