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Biochemical Network Analysis group, led by Jürgen Zanghellini . The team focuses on mathematical modeling, artificial intelligence, and high-performance computing to study metabolic networks and optimize
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the Mass Spectrometry unit of the Research Support Facility. Supporting data analysis for collaborators and optimizing sample preparation protocols. Helping to set up experiments, including preparing
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from outstanding candidates with expertise in Operations Research, Management Science and Business Analytics with a focus on fundamental research in combinatorial and stochastic optimization for business
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, stochastic optimization, modern machine learning methods, scalable algorithms for advanced Machine Learning techniques and explainable AI.In teaching, the position will contribute, inter alia
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peptides on cognitive functions, functional joint health, osteoporosis, and their optimal dosage. You will work at the exciting interface between clinical data collection and sports and nutritional research
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small organic molecules. The project investigates the use of machine learning and/or optimization methods to incorporate ligand and binding site information to enable efficient and robust virtual
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light or electrons are not well understood, which, for example, limits our ability to design photocatalyst materials that deliver optimal light absorption, catalytic activity, and energy transport
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to an exciting project at the intersection of data assimilation and optimal transport, focusing on Wasserstein Gradient Flows. The goal is to design innovative assimilation schemes using nonlinear approximation
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prior exposure to modern developments in at least one of these fields: functional analysis, numerical analysis of PDEs, mathematical foundations of deep learning, optimization, approximation theory
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to the ECH. You develop metrics for success and benchmarking frameworks to monitor and optimize all Hub programs and performance in line with its strategic goals. You are responsible for organizing and