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to benefit from latest advances in hardware technologies the ICON model was ported to run on Graphics Processing Units (GPUs) and is one of the first model that can be used in production
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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
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dedicated to research, advanced medical data streaming and processing machines, as well as state-of-the-art local and scalable cloud-based, compute infrastructure (CPU, GPU). Long-standing and very successful
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development (e.g., PyQt, Tkinter) CUDA for GPU acceleration Scientific computing libraries such as NumPy and SciPy A keen interest in scientific computing, atmospheric sciences, or advanced instrumentation is
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reactions. Development and deployment of graphical user interfaces (web apps, desktop apps) Parallel computing (GPU & CPU). Familiarity with cloud platforms (AWS, Azure, or GCP), Docker, Kubernetes