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techniques from optimization and control theory, scientific machine learning, and partial differential equations to create a new approach for data-driven analysis of fluid flows. The successful applicant will
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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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the Research Group “Nonlinear Optimization and Inverse Problems” (Head: Prof. Dr. D. Hömberg) starting as soon as possible. The project is part of a BMBF project concerning industrial scale data preparation
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topological magnetic structures in optical cavities. Work duties The main duty involved in this position is to conduct research, and the co-supervision of master's theses and doctoral students. Teaching may
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for the Natural Sciences. Preferred profiles include: probability or topology with an interdisciplinary orientation toward biology and other sciences, biomathematics, and modeling and simulation in medicine and
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the context of algorithmic problems related to constraint satisfaction and graph homomorphism and isomorphism problems. It brings to bear significant new mathematical (algebraic and topological) methods
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the context of algorithmic problems related to constraint satisfaction and graph homomorphism and isomorphism problems. It brings to bear significant new mathematical (algebraic and topological) methods
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and their by-products. o Monitor and control the migration of radionuclides and heavy metals throughout industrial processes. 2. Optimization of Industrial Processes: o Utilize nuclear
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Decision Intelligence for Supply Chain and Operations Optimization. The successful candidate will contribute to cutting-edge research at the intersection of Statistical Machine Learning and Generative
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Topology under Peter May) from the University of Chicago. Michael Rauchman - formerly GETCO's CTO, head of Americas equities, and global head of ForEx. As a hands-on leader, Michael was instrumental in