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, robotics, and industrial systems. Building on recent work in reachset-conformant identification, the project focuses on constructing abstractions whose reachable sets provably overapproximate all observable
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research questions include: ● How can AI, modern management, and advanced measurement redesign project workflows? ● Can we build an AI Coach to support practitioners in the field and scale expert
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-MS. (previous works e.g.: https://www.nature.com/articles/s41563-019-0555-5). Ideally, the applicant takes over the co-supervision of selected PhD candidates. We are looking for an individual with a
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, support for additional training, help to build an outstanding publishing record, and opportunities to teach and (co)-supervise MA students. The project offers funding for conferences, fieldwork, and
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machine learning-based systems to integrate more renewable energy into our energy systems and make energy use more efficient. We develop new optimization methods, machine learning algorithms, and
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group ‘Crop Physiology’ is to understand the physiology of plants down to the structure and function of genes and proteins. Thereby, relevant mechanisms are identified, which allow optimizing
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practical and laboratory tests as well as sophisticated simulations. We not only publish research results gained at numerous conferences and in journals, but also make our software available in open source
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on the nanoscale (previous works e.g.: https://www.nature.com/articles/s41563-019-0555-5). You will also supervise one PhD student who will work on a complementary topic guaranteeing quick output and an ideal
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complex tasks despite its simple make-up, consisting of only one giant cell that harbors millions of cell nuclei. Nuclei interact and may even kill each other when organisms fuse. But nuclei also act