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monitor Slurm-based training and evaluation jobs Debug failures related to distributed execution, checkpointing, filesystem performance, networking, and GPU utilisation Help maintain reproducible
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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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have used version control. Experience with high-performance computing, GPU programming, C/C++, or numerical methods for wave propagation, fracture, or solid mechanics is an advantage You are curious
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processing Very good programming skills (C, Matlab/Python, TensorFlow/PyTorch) and a passion for both theoretical and practical work are a premise The ability to develop own lines of thought, to
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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
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experience operating Linux systems in production environments Experience automating repetitive operational tasks Experience with shell scripting (Bash) and programming in at least one language such as
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, Machine Learning, Medical AI, Biomedical Informatics, Computational Biology, or a related field Strong programming skills in Python and modern ML frameworks Experience with deep learning and large