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Position 1 - Hybrid Traffic Flow Modelling This PhD focuses on developing hybrid traffic flow models that combine physical modelling principles with machine learning approaches, such as Physics-Informed
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, resulting in inconsistencies across soil properties and underperformance in data-scarce regions. This PhD project will develop next-generation machine learning methods for geospatial prediction by integrating
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& Spatial Planning, Physical Geography, and Sustainable Development. The team of the Department of Physical Geography excels in research and education on BSc, MSc and PhD level. We research processes
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PhD Studentship: Efficient Long-Horizon Task Execution in Physical AI (deep learning, computer vision, robotics) Number of awards: 1 Award information: Fully funded PhD studentship covering Home
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31.07.2026, Academic staff We are seeking a researcher in Scientific Machine Learning (SciML) to join the project "Data science at scale" at the Technical University of Munich, Germany. Ideal
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within a Research Infrastructure? No Offer Description We are now looking for a PhD student in machine learning with a focus on generative modeling and data-centric strategies for data-efficient machine
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) and/or machine learning (about 10 PIs). The Physics Laboratory is about 180-member strong and conducts world-leading research on a broad range of topics, including quantum technology, statistical
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of Basel. The successful candidate will be part of a cohort of 4 PhD students, 2 research engineers, and 7 principal investigators, working collaboratively across partner institutions. The project will be
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The continuous release of Earth Observation (satellite) data and the emergence of Machine Learning methods open up new possibilities for understanding forests. These large datasets provide
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in order to steer the emergent behavior of a collective. The base of the PhD project will be numerical simulations of active systems, where machine learning will be used as a tool to better understand