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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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expertise. You will collaborate with other PhD students and postdocs within the group. Our research covers a range of topics—from microbial ecology theory to process engineering. We develop new treatment
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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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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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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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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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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