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collaborative technical environment responsible for operating and developing a research infrastructure comprising CPU- and GPU-based HPC systems, petabyte-scale storage, scientific web services, secure
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outstanding 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
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experience with HPC environments: job schedulers such as Slurm, shared filesystems, and multi-node GPU systems Strong Linux systems and container skills (Docker/Podman and HPC runtimes such as enroot or
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physics, engineering, computer science, or a related field. Strong scientific programming in Python and experience with GPU processing of large-scale datasets. Experience with inverse problems and 3D
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for tomography, laminography, or a closely related modality. Strong scientific programming in Python and experience with GPU processing of large-scale datasets. Excellent written and oral
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academic team and opportunities to work with world-class researchers • Access to state-of-the-art computing infrastructure (Swiss AI large scale GPU cluster) • Opportunities to publish at top-tier
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models without performance degradation (FP8, INT4, AWQ, GPTQ) and evaluating quantised models Experience with LLM evaluation harnesses and benchmark pipelines Familiarity with GPU inference performance
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Experience running evaluations at scale on GPU clusters (Slurm or similar) and with inference engines such as vLLM or SGLang Familiarity with agentic evaluation and agentic harnesses: tool use