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you to contribute. Our activities integrate computational mechanics and advanced materials technology, combining multidisciplinary expertise to predict, understand, and elevate the performance
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understand why species and populations differ in their vulnerability, so that research can inform conservation, management, and policy. However, we still cannot reliably predict how climate change will affect
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-based multispectral, hyperspectral, and RGB imagery. These models will integrate crop growth models, agronomic data, and genomic information to predict key wheat traits under varying climate conditions
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enrich the knowledge base (i.e. learning by interaction); (iii) querying the knowledge base about what was useful in the past to predict actions that might be useful in the present, try them out and update
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and optimization, including model predictive control and reinforcement learning. The aim is digital-twin-based decision support for electrified groundwork construction. The position is placed
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to convert biomass into valuable products. We use advanced computational technologies to discover how biomolecules and organisms function and interact. We pioneer new methods for prediction, prevention
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for the diagnosis and prediction of lithium-ion battery ageing. About us At the department of Electrical Engineering research and education are performed in the areas of Systems and Control, Communications, Signal
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machine learning for neuromorphic computing for predictive materials discovery. The doctoral student will be part of a larger project Brain-inspired AI Design of Topological Magnets for Sustainable