190 machine-learning "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" positions at Technical University of Munich
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skills, ability to interact with scientists at different levels good software design skills and the ability to write clean, and reusable code in machine learning, deep learning frameworks, such as
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on the nanoscale (previous works e.g.: https://www.nature.com/articles/s41563-019-0555-5). You will also supervise one PhD student who will work on a complementary topic guaranteeing quick output and an ideal
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(MDSI) is an integrative research institute at the Technical University of Munich (TUM), with an interdisciplinary and cross-faculty focus on data science, machine learning, and artificial intelligence
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inspiring international environment and to learn from some of the world's leading researchers · Development of own international industrial and academic network · Independent working
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research and work environment within a young and dedicated team · An exceptional opportunity to experience research in a highly inspiring international environment and to learn from some of the world's
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coding skills to design highly efficient algorithms. Solid knowledge in the areas of algorithmics, optimization problems, as well as experience with SAT/SMT solvers or machine learning is an advantage
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Bewerbung, abrufbar unter https://portal.mytum.de/kompass/datenschutz/Bewerbung/. The position is suitable for disabled persons. Disabled applicants will be given preference in case of generally equivalent
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Freising, Germany Tel. +49 8161 71 3961 patrick.bienert@tum.de https://www.mls.ls.tum.de/en/cropphys/home/ www.tum.de The position is suitable for disabled persons. Disabled applicants will be given
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be achieved, for example, by developing learning algorithms and bringing together different sensor systems in the vehicle and on the road. Where you put the focus - that is up to you. The concepts
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, imaging). • Solid foundations in signal processing and statistics. • Experience with machine learning for regression (e.g., tree-based methods, neural networks) • Hands-on experimental skills: ability and