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candidates working in formal methods and/or computer systems with interests in one or more of the following areas: machine-checked verification of systems software, GPU kernel semantics and verification, and
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systems in terms of development effort, features, and performance. Its missions include the design and implementation of verified inference components, the formalization of GPU kernel semantics, the
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for operating and developing a research infrastructure comprising CPU- and GPU-based HPC systems, petabyte-scale storage, scientific web services, secure environments for sensitive data, and emerging AI-related
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microscopy, X-ray imaging, and light microscopy). It supports researchers across three compute scales: laptop-based prototyping (S-Gym), single-GPU workstations (M-Gym), and multi-node HPC at the National
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, networking, and GPU utilization. Help maintain reproducible training recipes, configuration files, launch scripts, and documentation. Work with researchers and CSCS engineers to improve the reliability and
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, GPU) and workshops for mechanical, electrical and electronic development projects. Long-standing and very successful cooperations with industry and clinical partners (cardiology, radiology) offer
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research-focused HPC environment. Project background We train open foundation models with hundreds of billions of parameters on thousands of GPUs on one of the largest AI-ready supercomputers in Europe
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. We train open foundation models with hundreds of billions of parameters on thousands of GPUs on one of the largest AI-ready supercomputers in Europe. The team counts more than a dozen full-time
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, over 20 dexterous robotic hands and 12 robot arms for real-world data collection and teleoperation, high-resolution tactile skins, and a dedicated GPU cluster with eight NVIDIA H200 GPUs alongside access
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efficient inference. Experience with GPU computing and high-performance or distributed computing environments. Exposure to planning, scheduling, workflow engines, or reasoning systems, and to scientific