73 phd-studenship-in-computer-vision-and-machine-learning PhD positions at Technical University of Munich
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body organs. We are looking for a PhD student (m/f/d) to start at the TUM between now and March 2023. Your Task From animals’ circulatory system to random porous media in fuel cells, morphological
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of AI. The ideal candidates will have a background in computer science, statistics, mathematics, or related fields, as well as an interest in social science research methods and theories. The PhD
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23.09.2024, Wissenschaftliches Personal The research group of Prof. Marc Schmidt-Supprian at the Institute of Experimental Hematology is seeking a highly motivated PhD student starting from now
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28.11.2024, Wissenschaftliches Personal Three funded PhD student positions are available at the Chair of Plant Systems Biology at the School of Life Sciences of the Technische Universität München in
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research and travel budget available to best support your research. You will partic-ipate in teaching and supervising students, interact with and learn from the other team members, and re-ceive close
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distribution within body organs. We are looking for a PhD student (m/f/d) to start at the TUM in September or later. Your Task From animals’ circulatory system to random porous media in fuel cells, morphological
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the testing of newly devel-oped materials and the use of machine learning methods to process complex data sets. The focus is on techniques such as ultrasound, radar, computed tomography, acoustic emission
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mathematics, (theoretical) computer science, machine learning foundations, electrical engineering, information theory, cryptography, statistics or a related field. - Advanced knowledge of probability theory
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PhD position | Sustainable Energy Materials | Electrochemistry 30.06.2023, Wissenschaftliches Personal We test novel catalysts for sustainable energy conversion processes such as polymer electrolyte
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group, a multinational insurance company. Tasks Your duties will include: Literature research Designing, implementing, and evaluating novel machine learning approaches to detect building attributes from