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languages Knowledge of one or more of the following, with strong motivation to grow in the others: GPU programming (CUDA, Triton, PTX) or high-performance computing ML inference or serving systems (vLLM
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languages, systems, or ML Research experience in one or more of: interactive theorem proving (Rocq, Lean, HOL, Isabelle, or similar), GPU programming or semantics, compilers, concurrency, or ML systems (e.g
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science or Physics You have developed a keen interest in medical imaging physics, signal and data processing Very good programming skills (C, Matlab/Python, TensorFlow/PyTorch) and a passion for both theoretical and
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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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researchers across ETH accelerate their research with frontier AI. You will partner with research labs to understand their scientific challenges and co-develop agentic solutions: multi-step pipelines that plan
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Researcher (R1) Application Deadline 18 Nov 2026 - 22:59 (UTC) Country Switzerland Type of Contract Temporary Job Status Part-time Is the job funded through the EU Research Framework Programme? Not funded by a
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science, conversational AI, and marketing analytics and on substantial investments in on-premises GPU infrastructure that allows us to work with confidential partner data under full data control. The focus
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, engineering, computer science, or a related field. Experience with inverse problems and 3D reconstruction methods for tomography, laminography, or a closely related modality. Strong scientific programming in
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scientific programming in Python and experience with GPU processing of large-scale datasets. Experience with inverse problems and 3D reconstruction methods for tomography, laminography, or a closely related