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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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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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: - QUANTITATIVE VERIFICATION: analysis of probabilistic systems (Markov decision processes, stochastic games, chemical reaction networks), automata theory and temporal logic, machine learning in verification
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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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projects in basic biomedical research and who wish to learn methods relevant to their current research. To this end, the grant finances the participation in practical training courses or short-term research
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graph processing frameworks, graph databases, graph learning frameworks, knowledge graph systems, and graph mining systems. While each of them is specialized on a specific type of task and is highly
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of empirical research (quantitative or experimental) methods, • knowledge of statistics, programming languages (e.g., Python), natural language processing, machine learning is advantageous but not
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University Munich (www.tum.de). Accordingly, we are currently searching for PhD Students and Postdocs to join our team! PhD Students For PhD students, we are looking for persons that are willing to learn and
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PhD/Postdoc position in trustworthy data-driven control and networked AI for rehabilitation robotics
learning • robotics and/or mechatronics • computer languages C, C++ and Python and interest to work in an interdisciplinary environment are desired. German language skills are necessary for this position