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interdisciplinary perspectives excellent communication skills in English (both written and oral) a willingness to engage with and learn the programming languages and software tools necessary for the modeling work. We
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to supervise PhD, Master and Dr. med (thesis as part of medical studies in Germany) students. The fellow will also have the opportunity to teach as part of the institute’s Masters and doctoral program but will
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of Pathology (https://www.pm.mh.tum.de/path ), and is affiliated with the TUM School of Computation, Information and Technology (CIT, https://www.cit.tum.de/ ), the TUM School of Medicine and Health (MH, https
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contribute to practical supervisory implementation Your Profile: Enrolment in a Ph.D. programme or Ph.D degree or Masters with 3-4 years of relevant professional experience in Economics, Sustainable Finance
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to teach as part of the institute’s Masters and doctoral program but will not be required or expected to do so. The salary is paid according to the German TV-L system (the salary agreement for public service
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Methodological competence: Strong programming skills, ideally including experience in deep learning and/or high-performance computing Passion for method development Very good English communication skills – both
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performance and degradation of electrolysis in dependence on different operating modes Your Profile: Completed Master’s degree in chemical engineering, computational engineering, computational mathematics, data
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, Statistical Physics, Genome Annotation, and/or related fields Practical experience with High Performance Computing Systems as well as parallel/distributed programming Very good command of written and spoken
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doctoral degree in psychology, cognitive science, biology, computer science, medicine or an equivalent discipline Relevant research experience or interest in the field of “Decision Neuroscience
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good publication track record Above-average master’s degree in computer science, electrical/ mechanical engineering, applied mathematics, or a similar engineering-oriented quantitative discipline