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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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covers a range of topics—from microbial ecology theory to process engineering. We develop new treatment processes and investigate the fundamental science behind them. Examples of our research areas include
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research spans microbial ecology theory to process engineering, and includes the development of new biotechnological processes. Examples of previous research areas include: Anammox Fermentation Microbial
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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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spaces of AI models will be analyzed in their facts using quantitative methods from data science and AI, results of which then be investigated using qualitative methods and theory of critical studies
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behavior of programs at a high level. Automata theory — to manipulate logical formulas and domain representations. Two-player games — to reason about strategies and synthesized programs. The work involves
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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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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