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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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Do you have experience with modelling structures subjected to dynamic loading? Are you interested in data-driven methods for modelling applied loading? Are you eager to share your knowledge within
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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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Do you have experience with modelling structures subjected to dynamic loading? Are you interested in data-driven methods for modelling applied loading? Are you eager to share your knowledge within
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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 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
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patterns, and extreme climate events remains a subject of debate. Using a combination of climate modelling, statistical methods, and machine learning, ArcticPush aims to uncover the conditions under which
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patterns, and extreme climate events remains a subject of debate. Using a combination of climate modelling, statistical methods, and machine learning, ArcticPush aims to uncover the conditions under which