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Field
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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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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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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
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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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theoretical modeling, numerical simulation, and experimental validation. Key objectives can include: Developing theoretical and computational models for friction-induced damping in joints and interfaces
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developing new explanation methods. This will involve using tools from mathematical machine learning theory to prove mathematical guarantees about the performance of such new explanation methods, as
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, which is time-prohibitive and therefore impractical for large parts. This project therefore aims to develop numerical methods that enable the efficient simulation of the LAFP process while accounting
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The PhD will develop AI methods and approaches to enable accurate characterization of metal scrap, for more efficient metal recovery and recycling Job description The volume of available metal scrap
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and optimizing post-plasma catalysis strategies, we want to create an energy-efficient and scalable method that reduces greenhouse gas emissions and supports the transition to a carbon-neutral chemical