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the use of hierarchical graph neural networks for modeling multi-scale urban energy systems. By combining advances in Physics-Informed Machine Learning (PIML) and Graph Neural Networks (GNNs) with real
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) approaches. Design predictive maintenance algorithms using machine learning, statistical learning, and digital twin-based models to anticipate failures and optimise maintenance interventions. Integrate AI
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), biostatistics, machine learning, data science and research data management, and causal inference methods (Iris Pigeot, Marvin Wright, Vanessa Didelez), and etiologic and molecular epidemiology (Konrad Stopsack
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collaborations and perform cross-species comparisons. We use machine learning techniques for neural data analysis and computational modelling with a special interest in biologically-inspired deep learning and AI
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candidate will perform prioritized Non-Targeted Assessment across diverse water matrices and case studies, while the AI4Science PhD will develop machine‑learning models that learn from and build upon
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of system and data confidentiality and complete any other requirements. Desirable skills: Experience in programming (C, python, or similar). Knowledge in machine learning or distributed systems. How to apply
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Systems, Tübingen/Stuttgart in Germany. Together we want to build a lighthouse for machine learning and modern artificial intelligence in Europe. The cooperation spans all levels, from leading experts
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programme at the Faculty of Science . The ideal candidate has a background in or experience with one or more of the following topics: Advanced deep learning architectures Mathematical foundations of machine
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for a/an University assistant predoctoral - PhD Position in Graph Learning 39 Faculty of Computer Science Startdate: 01.05.2026 | Working hours: 30 | Collective bargaining agreement: §48 VwGr. B1
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master’s degree with academic qualifications in digital health, data analysis, and/or machine learning applied to health research. Admission to the PhD program requires a 120 ECTS master’s degree, including