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postdoctoral position within the Q-VERSE EU project, which aims to provide a ready-to-use quantum software toolkit that supports various APIs and HPC interfaces in an open and transparent manner for Europe’s
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-learning oriented, and the group has access to excellent HPC infrastructure. For more information about the Language Technology Group (LTG) at IFI, please see: http://www.mn.uio.no/ifi/english/research
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perform sedimentary ancient DNA laboratory work together with the other team members. In addition, she/he will administrate the EcoArch project on our HPC cluster and assist with the bioinformatic analyses
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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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quantum computing, aiming to solve small, complex sub-problems using Quantum Process Units (QPUs) while leveraging classical High-Performance Computing (HPC) on data-intensive tasks. With dedicated
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working in high‑performance computing (HPC) environments, and prior experience with post-training, fine-tuning, or training large language models (LLMs) is an advantage, but not a requirement. Experience
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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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. Strong (inter-)national network in field of application. Experience with high-performance computing (HPC) and large datasets. Experience with machine learning applied to geophysical signals. Experience in
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optimization for the compute continuum, across cyber-physical systems (CPS), distributed artificial intelligence (AI), and high-performance computing (HPC), from the data center to the edge devices