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have expertise in at least one of the following research areas: PDEs, numerical methods, optimization, functional analysis, or stochastic analysis Candidates without a master’s degree have until 1st
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a single method for anisotropic flow modelling for both ice and olivine, by mapping CPO parameters directly to anisotropic viscosity parameters. This technique should reduce the computation complexity
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demonstrate strong theoretical and methodological capacities as well as documented expertise in computational methods. Experience with high performance computing is strongly preferred. Experience from applied
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in-depth qualitative analyses but also mixed-methods approaches, possibly enabled by emerging AI-enhanced techniques. The PhD project should overall contribute to a better understanding collaborative
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development of computer systems for data analysis, development of machine learning methods, and the clinical use of technology. Within the research groups you will therefore work together with computer
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-prediction benchmark studies. Depending on the qualifications and preferences of the candidate, the work may entail experimental investigations and/or modelling in the open-source computational fluid dynamics
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in the open-source computational fluid dynamics (CFD) code PDRFOAM. The work will be conducted in collaboration with other research projects on hydrogen safety at the department. The position offers a
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employment period is three years. A premise for employment is that the PhD Research Fellow will be enrolled in USN's PhD-program in Technology within three months after accession. About the PhD-project
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should have hands-on experience with a broad range of methods in molecular biology, cell biology, and biochemistry that may include mammalian tissue culture, immunoprecipitation and confocal microscopy
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there is a premise for employment that the PhD Research Fellow is enrolled in USN’s PhD-program in Technology within three months of accession in the position. About the PhD-project Offshore wind energy