37 computer-science-programming-languages-"UCL"-"UCL" Postdoctoral positions at Leibniz
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accessibility by public transport or car (including free parking) 30 days of vacation Participation in the benefits program for employees („Corporate Benefits“) The position is available for 3 years. We look
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written English and German (minimum B2 Level) We offer: You will become part of a world-leading research institute dedicated to the biology of aging and have the opportunity to join a very well-equipped Lab
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good communication and organizational skills, and ability to work in an independent and flexible manner PhD degree in the Life Sciences Very good English language skills (oral and written) What we offer
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” or “magna cum laude” acc. to the German grading system) in sociology, social anthropology, human geography, political science or another relevant discipline. A proven scientific focus on the political right
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(IWH-DPE ). Knowledge of German is not a requirement for the position. Further information The IWH is a family-friendly employer that guarantees equal opportunities. We highly appreciate diversity and
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Excellent command of advanced statistical methods, such as network analyses, using the R programming language Strong publication record relative to the career stage, demonstrating research excellence and
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in Europe, we combine interdisciplinary basic research with science transfer to the public, the media and policymakers. Since 2009, the PRIF has been a member of the Leibniz Association, a national
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, interdisciplinary teams, value diversity of thought, and enjoy the freedom to explore novel ideas. You communicate fluently in English, both orally and in writing. We Offer: Access to an international network
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(MPPT). Your profile: We are looking for motivated individuals (m/f/div) with a Ph.D. degree in Electrical Engineering, Materials Science, Physics, or a related field. Candidates should have a proven
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-party research funding are expected. We are particularly interested in a candidate in any field of economics who leverages state-of-the-art machine learning and causal inference methods to innovative