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Field
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qualification program for PhD students containing excellent multidisciplinary training with tailor-made subject-based and soft skills courses, annual retreats, summer school, and a supervision concept. More
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documents. Please send them by e-mail to marius.henkel@tum.de Technical University of Munich Prof. Dr.-Ing. Marius Henkel | Assistant Professorship of Cellular Agriculture | TUM School of Life Sciences Gregor
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teaching activities and/or supervise Master's students Requirements: A Master's degree (or equivalent) in Computer Science, Statistics, Mathematics, or related fields Strong programming skills (Python, Java
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techniques. User of engineering software and modelling languages, such as, Python, MATLAB Toolbox, LabVIEW, DaisyLab, C++….. Working in interdisciplinary environments, good contextual understanding
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, computer science or quantitative criminology; The diploma requirements must be met by the time of appointment. • Intermediate programming skills (e.g., Python or R), as well as a willingness to develop these further
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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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, this research will offer unique knowledge for tailoring intervention and prevention programs in several youth contexts. This project is proudly supported by the youth platform JEZ!, and is a collaboration between
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programming. You are highly motivated to conduct (applied) research at the intersection of (deep) machine learning and the health sciences. You have good programming skills in languages such as Pythorch, and
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international teams and have programming skills. In addition, you are able to express yourself confidently both orally and in writing in English. What we offer We offer a three-year contract with the possibility
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internationally. Job profile You hold a Master’s degree in computer science, electrical engineering, (technical) cognitive science, Human-Computer Interaction or areas relevant to the research topic (for example