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We are offering a PhD student position in machine learning (ML) theory, focusing on new methods for training models with a limited amount of data. The student will be a part of a new NEST initiative
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Engineering and Autonomous Systems division . We offer advanced PhD courses where we extend the fundamentals in optimal control, machine learning, probability theory and similar. The research and learning
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focusing on: Quantum mechanical calculations using density functional theory. Mean-field modeling and Monte Carlo simulations for reaction kinetics. Theoretical spectroscopy By combining quantum mechanical
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of robotics, electromobility and autonomous driving. We offer advanced PhD courses where we extend the fundamentals in optimal control, machine learning, probability theory and similar. The research and
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: The successful candidate must have a Master's degree in electrical engineering, engineering physics, or related disciplines. Completed courses in signal processing, radar or communication theory are meritorious
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related to the research project, including an interest in connecting theory and practice for understanding the relationships between science, politics, power and social justice. Fluent level of English
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both qualitative and quantitative methods, e.g., case studies, interviews, applied analytics, and field experiments. By developing new theories and applications, you will have the opportunity to solve
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correctly. Project description The goal of this PhD project is to develop techniques for the design and verification of assured ACPS with a focus on runtime assurance. You will develop theory and tools