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supercomputer hardware. The research work will be informed by collaborations with the scientific computing team that manages the local HPC resources at NTNU, and Uninett Sigma2, which manages both the Norwegian
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work will be informed by collaborations with the scientific computing team that manages the local HPC resources at NTNU, and Uninett Sigma2, which manages both the Norwegian national infrastructure
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advantage but not mandatory. Experience with relevant neuroimaging software, secure research infrastructures (e.g., SAFE or HPC), and experimental platforms such as PsychoPy is also considered an advantage
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to be able to move toward computational devices (HPC-simulations, and Synchrotron, MOKE and MFM experiments) based on nanomagnetic metamaterials. Intermediate the research focus is toward basic
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convergence of high-performance computing (HPC) and AI, which is a subject that sees an increasing importance due to the widespread use of AI and in particular machine learning (ML). As today’s mainstream AI/ML
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events and in machine learning for the earth system is required Strong and demonstrated programming skills are required Prior experience with geospatial data analysis in Python, working on scientific HPC
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. Place of work is Department of Informatics at 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
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optimization Familiarity with continuous-variable/bosonic systems, non-Gaussian states, multimode entanglement, or Wigner-function methods Experience with HPC workflows (cluster computing, GPU computing
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/bosonic systems, non-Gaussian states, multimode entanglement, or Wigner-function methods Experience with HPC workflows (cluster computing, GPU computing, reproducible pipelines) Track record
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– Zebrafish Core Facility, Chemical Biology Platform, single-cell and long-read sequencing technologies, an HPC/data-science hub, and access to UiO infrastructures. Full details of the core facilities at UiO