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-mode taxonomies). Implement and maintain high-quality research codebases (PyTorch/HF), experiment tracking, and compute workflows (multi-GPU, HPC/cluster), ensuring reproducibility and documentation
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deploying and maintaining HPC clusters. Proficient in coding (Python, C/C++, Lua) with a focus on clean, efficient code. Strong skills in Linux administration, automation, and networking (TCP/IP, HTTP
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Python, NCL, and MATLAB. Should have proficiency in scientific computing, high-performance computing (HPC), and scripting (e.g., Bash, CDO/NCO). Should have strong quantitative and analytical skills in
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, numerical modelling, and use of HPC environments Proven ability to manage and analyse large-scale datasets from simulations or experiments Solid background in theoretical physics, with at least foundational
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, linux and HPC. A strong interest in interdisciplinary research. Excellent interpersonal skills to work effectively with group members and collaborators. Good oral and written communication skills in
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genomics and functional interpretation of genetic variants. Proficiency in Python, R, or other bioinformatics languages. Knowledge of cloud computing, and high-performance computing (HPC) environments