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dynamic Electrochemical Impedance Spectroscopy (EIS), combining advanced measurement technology with modelling and data-driven analysis. A key component is a novel measurement approach that enables high
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. You will develop dynamic models and apply them, for example, to analyze sociotechnological networks and to model interactions between humans and AI agents (such as LLM-based chatbots and autonomous
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-language-action models (VLA), specifically the handling of uncertainty in VLAs. VLAs have the potential to simplify system design in robotics and autonomous driving, both through verbal user interfaces, and
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application! Data-driven life science (DDLS) uses data, computational methods and artificial intelligence to study biological systems and processes at all levels, from molecular structures and cellular
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for efficient last-mile deliveries with a focus on climate and flexibility. Using advanced modeling and data analysis, you’ll create solutions that make final deliveries smarter and more sustainable. Your work
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principled new models and methods, for modern machine learning problems. Machine learning recently has been largely advanced by differential equation-based frameworks, such as generative diffusion models
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, solvent-free dosage, as well as recent advances in in situ polymerization of soft, substrate-free neural electrodes, this doctoral project will investigate and develop novel implantable technologies
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emissions and biodiversity impacts related to bank lending, particularly in agriculture. Therefore, banks therefore often must rely on coarse standard values or low‑precision models, making it difficult
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and education in both statistics and machine learning, at the undergraduate, advanced and PhD levels. STIMA is characterized by a modern view of the statistical subject, where probabilistic models
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-critical intelligent machines, e.g. collaborative robots, rescue drones, or vehicles with advanced driver-assistance systems, with cloud-assisted AI and control systems. A main challenge in all cloud