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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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overcomes the geographic limitations of conventional systems, enabling global scalability and accessibility. Using advanced computational fluid dynamics (CFD) approaches, the project is aimed at advancing
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experience in computational modelling. It will involve the use of open-source computational fluid dynamics codes, with turbulence modelling and porous media approaches. It will also require the development
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research team. Good knowledge and experience in heat and mass transfer is essential and proficiency in the use of Computational Fluid Dynamics will be considered an advantage. The student will benefit from
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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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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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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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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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Physics , QCD , Quantum chaos and thermalization , Quantum Computation , Quantum Computing , Quantum Condensed Matter Theory , Quantum Control , Quantum Devices and Sensing , Quantum Dynamics , Quantum
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We are seeking an outstanding candidate for a PhD fellowship in the field of computational fluid and solid mechanics. The fellowship will start on September 1st, 2025, or as soon as possible after