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-temporal machine learning method development, including: generative models for grid-based and particle-based spatio-temporal data; controlled generation methods for data assimilation; and graph-based multi
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, numerical analysis, approximation theory, or equivalent subjects. Alternatively, you have gained essentially corresponding knowledge in another way. Candidates should have experience in the following
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on the intersection of robotics and control theory. Project description: This PhD project aims to develop learning‑based methods that combine expert demonstrations with experiential reinforcement learning to enable
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engineering (focusing on deep learning for computer vision), and the division of statistics and machine learning at the department of computer and information science (focusing on the theory behind machine
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methods for data assimilation; and graph-based multi-scale neural network models. While the developed methods will be broadly applicable, particular emphasis will be put on the problem of inferring gas