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these nanocomposites, we are looking for a postdoc to further develop high performance computing numerical methods in our state-of-the-art open source micromagnetic model, MagTense. MagTense is based on a core
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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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image quality in time-resolved micro and nano CT data. Develop and apply numerical implementations in simulation studies as well as to large experimental micro and nano CT data. Explore multi-modality
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. The position focuses on frequency-domain electromagnetic (FEM) and transient electromagnetic (TEM) methods. The successful candidate will contribute to the development of an inversion framework for the joint
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at the intersection of advanced probabilistic machine learning and microbial bioscience. This position offers a unique opportunity for developing novel probabilistic ML methods with a view towards
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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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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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, or a related field with a focus on groundwater or hydrological modelling Documented experience in numerical or data-driven groundwater modelling (e.g., MODFLOW) Proficiency in handling geospatial and