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                Employer- Delft University of Technology (TU Delft)
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                Vacancies PhD Opening: Reinforcement learning in human neuromusculoskeletal models for the control of human-inspired musculoskeletal robots. Key takeaways We’re seeking for motivated candidates 
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                Are you highly motivated to do PhD research in mathematical machine learning, with special emphasis on mathematical foundations for explainable AI? If yes, the Korteweg-de Vries Institute 
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                machine learning solutions to optimize the component lifecycle directly contributing to a more circular economy. Information In the manufacturing landscape, determining whether a component should be 
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                28 Oct 2025 Job Information Organisation/Company Eindhoven University of Technology (TU/e) Research Field Engineering » Computer engineering Engineering » Electrical engineering Researcher Profile 
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                project FoRECAST, you’ll work independently and in a collaborative, diverse team. This is a unique opportunity to contribute to the technological foundations for tomorrow’s machine learning. Your job In the 
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                boundaries of system-level modelling, analysis, design, exploration and synthesis beyond the current state-of-the-art? Or are you curious to learn more about the application of AI for system diagnostics and 
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                motivated to do PhD research in mathematical machine learning, with special emphasis on mathematical foundations for explainable AI? If yes, the Korteweg-de Vries Institute for Mathematics at the University 
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                Are you highly motivated to do PhD research in mathematical machine learning, with special emphasis on mathematical foundations for explainable AI? If yes, the Korteweg-de Vries Institute 
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                by a strong motivation are also welcome to apply. You are genuinely curious about the brain and enjoy learning beyond your comfort zone. In the absence of previous background in hardware, machine 
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                shifts in cell state and cell fate. Integrate spatial transcriptomics data to anchor these predictions in tissue context. Develop machine learning methods (e.g. graph neural networks, variational