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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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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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The role will develop new AI methods for identifying the instantaneous state of a fluid flow from partial sensor information. The research will couple techniques from optimization and control theory
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to avoid abrasion and agglomeration. A small-scale experiment will be devised to explore some of the complexities. There will be issues of supersonic flow and how the presence of an abrasive fluid affects
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to avoid abrasion and agglomeration. A small-scale experiment will be devised to explore some of the complexities. There will be issues of supersonic flow and how the presence of an abrasive fluid affects
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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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Proteins are key biomarkers, indicative of normal biological or pathogenic processes and responses to intervention. Identification and quantification of such molecules in biological fluids is
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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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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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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