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methods (such as Machine Learning, Metric Learning, Reinforcement Learning, Graph Representation Learning, Generative Models, Domain Adaptation, etc.) for Design Automation applications. To this end, we
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on the design and evaluation of innovative data- and machine learning-based systems to integrate more renewable energy into our energy systems and make energy use more efficient. We develop new optimization
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private machine learning: Differential privacy (DP) is the gold-standard for privacy protection, but deep learning models trained with DP suffer from privacy-utility trade-offs. You will develop novel model
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(e.g. via machine learning) to qualitative analyses (e.g. via interviews) to support ambitious policies for climate and energy transitions. This position Green hydrogen is key to decarbonizing many hard
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: M.Sc. in Computer Science, Machine Learning, or equivalent with interest in Medical Imaging and Deep Learning. Strong knowledge in Machine/Deep Learning with experience in discriminative models
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journals. Close collaboration with team members and colleagues. Essential qualifications: M.Sc. in Computer Science, Machine Learning, or equivalent with interest in Medical Imaging and Deep Learning. Strong
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the faculties of medicine and computer science at TUM, as well as the Munich Center for Machine Learning (MCML). It is a great place for interdisciplinary research between medicine and data science. We
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machine learning technologies. This PhD position is part of the project “Artificial Intelligence for the automated creation of multi-scale digital twins of the built world”, which is funded via the Georg
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, C++, etc.) Knowledge of machine learning, data mining, or related fields Excellent communication skills and ability to work in a collaborative team environment Interest in social science research
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: - QUANTITATIVE VERIFICATION: analysis of probabilistic systems (Markov decision processes, stochastic games, chemical reaction networks), automata theory and temporal logic, machine learning in verification