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Do you want to contribute to the future of AI-driven electric transport systems? Join our research group to develop advanced machine learning methods for electromobility, focusing on energy-aware
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are often software-intensive and we develop mechanisms to create better software for safer, more secure, and more usable systems. You will be supervised by Associate Prof. Rebekka Wohlrab, whose research
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to investigate and develop novel ceramic electrolytes for next-generation composite solid-state batteries. Particularly halides and sulfides are of interest. By integrating atomistic simulations with experimental
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, environmental analysis and education, we contribute knowledge about future sustainable land use and develop new solutions as a basis for decision-making in agriculture, forestry and the urban environment
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in research, but also for some higher education pedagogical training. Teaching may also be included in the duties. This position primarily consists of research tasks focusing on fundamental
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methods that combine soundscape targets, acoustic metamaterials, physical modelling, inverse design, machine learning and perceptual evaluation. The postdoc will develop models and design methods
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description Work on EU projects to develop next‑generation transport, emission and health forecasting models by integrating deep learning, xAI, and diverse data sources such as traffic sensors, smart‑card data
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disease models (e.g., AD/PD) Ability to develop independent research directions Preference will be given to applicants who have obtained their PhD degree no more than three years prior to the application
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. The overarching goal of the project is to further deepen the collaboration around practice-based school research together with our training schools. To make this possible, a postdoctoral position is planned with
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. Collaboration with industry and society is central to developing sustainable, resource-efficient and competitive solutions for the society of tomorrow. Employment information Employment: Temporary employment, 2