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Field
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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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metamorphic conditions, the exact mechanisms (dissolution–precipitation vs. dynamic recrystallization vs. mechanical transport vs. partial melting), the extent of mobility and role of fluids remain debated
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partners in the European project, in particular also with the research partner at the Royal Military Academy in Belgium, who is doing the Computational Fluid Dynamics (CFD) simulations to estimate
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(for plasma catalysis). Computational fluid dynamics & kinetic modelling of plasma reactor design. You will publish scientific articles related to the research project. You will carry out a limited number of
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Description TUD Dresden University of Technology, as a University of Excellence, is one of the leading and most dynamic research institutions in the country. Founded in 1828, today it is a globally
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to work independently within a dynamic research environment Willingness to collaborate with other research groups Excellent skills in written and spoken English You should strive for scientific excellence
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degree in mechanical, chemical, or energy engineering or similar and experience in some of the following areas: Experience in Multiphysics and CFD modeling involving fluid dynamics, and electrochemical
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element modeling, computational fluid dynamics). Knowledge of heat and mass transport processes in heat-sensitive materials and process optimization. Experience in supply chains and hygrothermal
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number of environmental challenges, including weather, climate and sea state forecasting. The Air-Sea Fluxes group at the Institute of Coastal Ocean Dynamics conducts laboratory- and field-based research
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sound background in geophysical fluid dynamics, experience in numerical ocean or atmospheric modelling, and experience with numerical data analysis. Good scientific presentation, writing, and