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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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systems, enabling global scalability and accessibility. Using advanced computational fluid dynamics (CFD) approaches, the project is aimed at advancing modelling capabilities for the prediction of energy
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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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, combustion, and process optimisation. The project is focussed on the development of novel interface capturing Computational Fluid Dynamics methods for simulating boiling in Nuclear Thermal Hydraulics
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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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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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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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applied physics other related disciplines. Demonstrated knowledge in at least one of the following areas: porous media flow computational fluid dynamics (CFD) pore-network modelling lattice Boltzmann method
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flow phenomena. The goal is to integrate theoretical and experimental fluid dynamics with modern computational tools to analyze and predict multiphase flow behavior. The project also involves applying