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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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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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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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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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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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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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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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Dynamics , Condensed Matter Theory , Cosmology , Crystallography , Dark Matter , Data analysis , EIC , Electron Hydrodynamics , electron-positron collisions , electron-proton collisions , Electronic Detector
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of the) [map ] Subject Area: Stochastic dynamical systems Appl Deadline: 2025/06/02 11:59PM (posted 2025/04/16, listed until 2025/06/02) Position Description: Apply Position Description Join Us! Are you
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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