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foundations. Candidates should possess an exceptional academic record and a strong mathematical background. Experience conducting large-scale computational experiments (e.g., multi-GPU systems) is advantageous
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optimization – with rigorous theoretical analysis. The ideal candidate has strong machine learning and AI expertise and is comfortable with – or eager to learn – large-scale multi-GPU experimentation
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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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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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signal processing and/or survey datasets. ML & AI techniques and applications. HPC and orchestration of scientific data processing workflows. Parallel computing (GPU & CPU). good software engineering
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responsibility for our unique GPU-accelerated 3D FDTD software suite and extending its capabilities Modelling the effects of atmospheric turbulence fields Software development (3D modelling and coding in Python, C
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metrics and usage statistics, identify inefficiencies on different levels (CPU/GPU, I/O patterns, etc.) and provide corresponding reports. You will work closely with researchers and HPC users and provide
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(CPU/GPU), numerical modeling/Monte Carlo simulations are an asset Visualisation skills are an asset Careful way of working, checking of results Candidates can have an M.Sc. degree in STEM, or a Ph.D
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the Alps supercomputer at the Swiss National Supercomputing Centre (CSCS), which features over 10,000 NVIDIA Grace Hopper GPUs, making it one of the most powerful AI-focused computing resources in