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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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invites applications for a postdoctoral position within a dynamic research team dedicated to understanding and improving taxation behavior through behavioral and experimental methods. This is a unique
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monitoring will be based on real time data streaming from the machine numerical control. The project will cover all the aspects related to the implementation and automation of the tool life cycle management
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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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, numerical, and experimental approaches. A central focus will be on improving the operational reliability of fluid power systems through early fault detection, diagnostics, and advanced monitoring techniques
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with statistical and numerical analysis methods as applicable to strain design problems is a distinct advantage. Familiarity with machine learning tools such as PyTorch, HuggingFace transformers and
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preferably has strong programming skills and experience with the modeling and simulations of fluid or solid mechanics or ice sheet flow and deformation (for example by use of finite element/volume methods
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section, which is part of the Department of Dramaturgy and Musicology, has numerous collaborations with local, national and international theatre makers, cultural institutions and other professional
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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