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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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/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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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
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collaboration with a team of experts at FZJ (INM-9: Institute of Neuroscience and Medicine - Computational Biomedicine, IBI-1: Institute of Biological Information Processing - Molecular and Cellular Physiology