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performance computing numerical methods in our state-of-the-art open source micromagnetic model, MagTense. MagTense is based on a core implemented in the Fortran programming language, and it relies
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. Qualifications: You should have (or be close to achieving) a PhD degree. Background within computational methods for inverse problems, ideally tomography. Experience with development of numerical implementations
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to realize a strong permanent magnet. To investigate this, you will use and further develop numerical methods in our state-of-the-art open source micromagnetic model, MagTense. At present, our ability to model
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to research and development projects with a focus on modeling and interpreting contaminant transport in the subsurface. Your focus will be on developing, testing and applying numerical models to investigate and
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, fluid mechanics, solid mechanics, materials science including phase changes, numerical mathematics and data analytics based on statistics and/or machine learning. Each of the postdoc positions will be