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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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of the German Armed Forces Munich), the DLR (German Aerospace Center) with its Oberpfaffenhofen institutes, and the BHL, the Bauhaus Luftfahrt. This pooling of research, graduate programmes and teaching merges
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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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-processing, and machine learning textual analysis of the full text of policy documents. Qualitative content thematic analysis is envisioned to compliment structural topic modelling to identify strategies and
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environment using machine learning technologies. This PhD position is part of our research on exploiting social media data for earth observation tasks. The work will be on the topic of developing geographically
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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