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PhD Position on Machine Learning Detection of Positive Tipping Points in the Clean Energy Transition
. This PhD will develop a machine learning module to detect early warning signals of positive tipping points from techno-economic data, helping policymakers design adaptive strategies for rapid and
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, this PhD will explore machine-learning (ML) methods to significantly reduce the turnaround time of SRS, thus enabling their use for industrial design processes. By combining state-of-the-art numerical
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mathematical control theory, and a keen interest in machine learning to apply for the PhD position within the Dynamics and Control section at the Department of Mechanical Engineering, Eindhoven University
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: vibration measurement and analysis, AI/machine learning or signal processing Programming experience (e.g. Python, MATLAB, or similar) A creative and analytical mindset with an eagerness to pioneer new methods
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. optimization and machine learning techniques) to prepare ports, terminals, shipping companies, and other port actors for this important challenge. Your research will be part of the PortCall.Zero project - a five
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to: Development of computer models of the musculoskeletal system incorporating neural control pathways Train NMS model control policies via RL Your seconday tasks will include: Using learned NMS model control
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8 Nov 2025 Job Information Organisation/Company Eindhoven University of Technology (TU/e) Research Field Engineering » Materials engineering Engineering » Simulation engineering Physics
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Vacancies PhD and EngD position for ESG-Based Decision-Making for Customer Portfolio Management Key takeaways We are seeking a highly motivated and creative PhD and EngD candidate to join our
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28 Oct 2025 Job Information Organisation/Company Delft University of Technology (TU Delft) Research Field Engineering » Computer engineering Engineering » Control engineering Researcher Profile
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individualized diagnosis and prognosis. As part of the ENSEMBLE Project—a multinational study funded by the Fondation Paralysie Cérébrale—you will help develop a machine learning-based multimodal prediction tool