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part of a new lab at the Delft Center for Systems and Control, supervised by Gabriel de Albuquerque Gleizer. The research will allow you to gain deep insights across nonlinear dynamics, optimization, and
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economy. Thermal interfaces at cryogenic conditions: Many advanced technologies — like quantum computers, powerful microscopes, and chip-making tools — require extreme cooling. However, the optimal design
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for molecular systems and thin films in combination with AI-driven process optimization. Help us shape the future of chemical research! Self-driving lab platform for photochemical and porosity research Join a
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with a strong background in applied mathematics, control theory and/or optimization to apply for a fully funded 4-year PhD position in the Smart Manufacturing Systems (SMS) group at the Engineering and
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such as Bayesian modelling and optimal control theory. Using state-of-the-art methods – including virtual reality, wearable sensing, motion platforms and advanced data analytics – you will place particular
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confidence in the measurements and establish traceable validation routes. Optimize the methods for realistic converter operating conditions and communicate practical guidance to academic and industrial users
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that could be highly valuable, for instance to consumers and patients wanting to monitor and optimize their health in a home setting, or to workers operating outside the reach of regular medical care and
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come up with jointly optimized schemes for such hybrid links and networks, incl. waveforms, modulation, coding and/or low-level protocols. You will further design and propose link architectures and
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its inverse reconstruction. A key challenge is the data-driven design of the experimental setup: exploring how the choice of measurements and configurations can be optimized to extract the most useful
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selection and validation, supervised and unsupervised learning, optimization techniques, (deep) neural networks, probabilistic methods and statistics, data visualization, natural language processing