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turbulent dynamics in multiphase environments such as the ocean surface. The project is part of the Collaborative Research Centre (CRC) “Energy transfers in atmosphere and ocean” funded by the German Research
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/duration 01.10.2025 for 4 years Job profile: Your tasks: European/national labour law or European/national social law Independent teaching Collaboration in research and teaching Training and further
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University explores synergies between nonlinear control theory and physics informed machine learning to provide formal guarantees on performance, safety, and robustness of robotic and learning-enabled systems
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groups in Amsterdam and Maastricht. As a founding member of the LHCb collaboration, the Nikhef team has a long-term involvement in both detector construction as well as operations. It also has a strong
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activities across these decentralised and increasingly complex networks. By deploying and advancing techniques such as machine learning, graph-based network analysis, and synthetic data generation, the project
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DTU Management’s Management Science division. The project is led by Professors Stefan Ropke and Richard Lusby and involves international collaboration with leading researchers in machine learning and
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for extracting physiological biomarkers from ECG, PPG, and related sensor data Machine learning and AI for predictive modelling and risk stratification Computational physiology modelling to personalise and
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Description TUD Dresden University of Technology, as a University of Excellence, is one of the leading and most dynamic research institutions in the country. TUD has established the Collaborative
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-informed design platforms. Furthermore, REUNATECH promotes trans-disciplinary collaboration via multi-stakeholder platforms, bridging academia, industry, and policy. Through its ho-listic, digital, and