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the controlled flow at tunable temperature and photopolymerization of the precursor. The practical work will be complemented by fluid mechanics computer simulations, including solutions employing machine learning
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project offers a unique opportunity to develop autonomous microswimmers, which are bioinspired structures at the micrometre scale that can propel themselves through fluids, mimicking natural swimming
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Requirements: very good or good university degree in physics, meteorology, fluid dynamics or comparable Description of the PhD topic: (subproject T7) In Urban air mobility, a high wind sensitivity of UAVs
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Technology and Logistics and co-supervised by at least one additional professor plus an international tutor of the RTG Requirements:very good or good university degree in physics, meteorology, fluid dynamics
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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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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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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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are developed, modelled and controlled. You will create novel adaptative, physics-informed models that tightly integrate thermo-fluid dynamic laws, deep learning neural networks, and experimental data. A key
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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