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Field
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in fluid flows related to the propagation of nonlinear waves and their properties.
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transfer learning to transpose the recurrent neural network (RNN) model available for supercritical CO2 power cycles to other cycles. Since thermodynamic conditions vary greatly depending on the fluid and
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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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a gas that is miscible with other fluids in the subsurface, and can react with minerals in the subsurface. These properties of CO2 will affect how CO2 moves in the subsurface, and it is important to
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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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working fluid. The proposed expander system consists of: - Membrane-based expansion chambers, ensuring efficient expansion without leakage risks; - A linear alternator, directly converting the reciprocating
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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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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