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We invite applications for a fully funded PhD position in the field of numerical modelling of iron electrodeposition, i.e., multiphase flows involving phase change, using fully resolved CFD methods
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-driven modelling and numerical mathematics leading to computationally fast methods State-of-X (where X is charge, health and/or function) estimation at the pack level. This requires developing
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to significantly reduce the turnaround time of SRS, thus enabling their use for industrial design processes. By combining state-of-the-art numerical methods and data-driven modelling techniques, the PhD candidate
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, this PhD will explore machine-learning (ML) methods to significantly reduce the turnaround time of SRS, thus enabling their use for industrial design processes. By combining state-of-the-art numerical
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, including thermal behavior and ageing and experiments that lead to accelerated ageing. This requires developing understanding of the underlying physics, methods for data-driven modelling and numerical
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accuracy requires high spatial and temporal resolution, which is time-prohibitive and therefore impractical for large parts. This project therefore aims to develop numerical methods that enable the efficient
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Are you fascinated by electromagnetic modeling and numerical problem solving? Do you want to contribute to the development of state-of-the-art metrology for integrated-circuit production
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accuracy requires high spatial and temporal resolution, which is time-prohibitive and therefore impractical for large parts. This project therefore aims to develop numerical methods that enable the efficient
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and simulation of PV systems and other offshore renewable energy technologies; hydrodynamic and fluid dynamic systems; programming and numerical modelling (e.g. Python, MATLAB); computational fluid
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of invariants of operator algebras, such as K-theory and cyclic homology; and Developing a mathematical method for passing from numerical Berry curvature to robust topological invariants in a large class of cases