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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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applicants for a 6-month paternity leave replacement who have a strong interest in using computational methods such as cognitive and psychophysiological modeling, (Bayesian) statistics and optimal experimental
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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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national and international partners Maintain, calibrate and troubleshoot high-end mass spectrometers with a focus on GC-HRMS and Orbitrap analysers Develop, optimize, and validate analytical GC-MS and LC-MS
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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