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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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, 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
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and selections of data) and fine-grained evaluation in the development of large language models. LTG members have access to large-scale computational resources through national and European HPC
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analysis workflows (Python and/or Julia-based; HPC-oriented handling of large datasets). Depending on competence: contributing to research software development supporting simulations and/or data workflows
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benchmarking of large language models (LLMs). The research profile of the group is heavily machine-learning oriented and the group has access to excellent HPC infrastructure. For more information about LTG
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written communication skills in English Desired qualifications: Experience in analyses and interpretation of clinical data in combination with microbiome or other -omics data. Experience with use of HPC
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: Experience in analyses and interpretation of clinical data in combination with microbiome or other -omics data. Experience with use of HPC clusters and workflow management tools (Snakemake, Nextflow). Strong
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members have access to large-scale computational resources through national and European HPC allocations, and this doctoral fellowship is expected to have a strong experimental component. Candidate
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for evaluation by the closing date. Only applicants with an approved doctoral thesis and public defense are eligible for appointment. Programming skills in GAMS and Python. Experience with HPC. Experience with
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related field. Demonstrated expertise in machine learning, deep learning, or advanced statistical analysis applied to complex, multimodal data. Hands-on experience with high-performance computing (HPC