57 neuroimaging-machine-learning-engineer PhD positions at Technical University of Munich
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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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of the Technical University of Munich. In our group, we advance ethical practice and theory in medicine, bio-medical technology, and public health, driven by the belief that embedding ethics is essential for shaping
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13.05.2025, Wissenschaftliches Personal The Professorship of Particle and Fiber Technology for Bio-Based Materials at Campus Straubing is looking for a PhD candidate (f/m/d) in Polymer Physics
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companies from all over the world, especially the USA, the UK, and Germany. Your Profile: Excellent university degree in engineering, chemistry, materials science, physics, electrochemistry or a similar
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20.07.2023, Wissenschaftliches Personal The Chair of Materials Engineering of Additive Manufacturing is looking for a full-time applicant as Wissenschaftliche/n Mitarbeiter/in (m/f/d) Founded in
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23.10.2023, Wissenschaftliches Personal The Technology and Innovation Management (TIM) Group at the TUM School of Management of the Tech-nical University of Munich, headed by Prof. Dr. Joachim
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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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or for multiobjective optimization problems. Implement the developed algorithms (e.g., in Python) and evaluate their practical performance on artificial and/or real-world data. Teach tutorials (in English) for
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, Computational Linguistics, Data Science or a similar field Good theoretical knowledge and practical experience with Natural Language Processing (rule-based and/or machine learning) Software Engineering Motivation
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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