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Computing (HPC) applications. Qualifications/Requirements Qualifications / Discipline: - PhD’s degree in Physics, Materials Science, Computer Science, Data Science, Artificial Intelligence, or a related
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statistics. Software frameworks: Excellent programming skills in Python, R or similar, with experience in frameworks such as PyTorch, TensorFlow, JAX, etc. HPC & Big Data: Proficiency in high-performance
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experience in working with Linux HPCs · Experience in applying machine learning methods to genomics data analysis · Experience in navigating public databases and genomics data repositories
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Blindern, Oslo. Job description This PhD project aims to study the convergence of high-performance computing (HPC) and AI, which is a subject that sees an increasing importance due to the widespread use
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learning methods to biological or clinical datasets. Proficiency in Python and R, with strong experience in Linux/HPC environments and workflow automation. Track record of publications in high-impact
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statistics. Software frameworks: Excellent programming skills in Python, R or similar, with experience in frameworks such as PyTorch, TensorFlow, JAX, etc. HPC & Big Data: Proficiency in high-performance
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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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cloud platforms for compute and storage. Version Control & CI/CD: Git, automated testing, deployment workflows. Experience with Linux systems, HPC, and distributed computing environments. Knowledge
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with Linux/Unix and HPC systems (SLURM) Experience with version control (Git/GitHub) Understanding of statistics for genomic analysis Preferred: Long-read sequencing analysis experience Proficiency in a
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, and diagnostics relevant to helioseismic inference. Development and maintenance of robust, reproducible analysis workflows (Python and/or Julia-based; HPC-oriented handling of large datasets). Depending