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prediction, signal tracking, fluid dynamics, and space exploration. Advancing Signal Modelling with Physics-Informed Neural Networks This project aims to develop Physics Informed Neural Networks (PINNs
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degree in Hydraulic Engineering, Hydropower Engineering, Civil Engineering, Fluid Mechanics or equivalent. Your course of study must correspond to a five-year Norwegian course, where 120 credits have been
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in control systems and fluid-structure interaction. The students will be supervised by Prof Khac Duc Do and the co-CIs of the project. Student type Future Students Faculties and centres Faculty
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Supervisory Team: Prof Neil Sandham PhD Supervisor: Neil Sandham Project description: This project is focused on scale-resolving simulations (large-eddy and direct numerical simulation) combined
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I offer projects broadly related to supernova explosions and the final stages in the lives of massive stars. Specific topics of interest include fluid dynamics processes in stellar explosions and
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28.07.2023, Wissenschaftliches Personal Prof. Karen Alim’s group on Biological Physics and Morphogenesis at the TUM Campus Garching uses theoretical and experimental methods to investigate how flow
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25.02.2022, Wissenschaftliches Personal Join the team of Prof. Karen Alim at the TUM Campus Garching to investigate how life emerged due to flows accumulating organic compounds in the early earth
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from a university with a focus on aerospace, physics, or related fields • Very good and fundamental knowledge in the areas of theoretical fluid mechanics and structural mechanics, as well as aero-thermal
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24.02.2020, Wissenschaftliches Personal The research group “Fluid Dynamics of Complex Biosystems” headed by Prof. Dr. Natalie Germann has an open PhD position in the field of experimental rheology
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, physics or related fields • Very good and fundamental knowledge in the areas of fluid mechanics and aero-thermal turbomachinery • High fascination for technical/scientific problems of numerical and