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„Research for a life without cancer“ is our mission at the German Cancer Research Center. We investigate how cancer develops, identify cancer risk factors and look for new cancer prevention
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different languages, to explore different transformational strategies for sustainable tourism. The specific tasks of this project would consist of document selection and review, data cleaning and pre
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reporting skills (4) Experience in spatial data analysis using geographic information systems (GIS) and programming languages (R, Python) as well as experience in numerical model applications and multivariate
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“Research for a life without cancer” is our mission at the German Cancer Research Center. We investigate how cancer develops, identify cancer risk factors and look for new cancer prevention
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Priority Programme SPP2311 (German Research Council), the Chair of Numerical Mathematics under the leadership of Frau Prof. Dr. Barbara Wohlmuth is seeking a candidate for a PhD or postdoctoral position (100
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, the University of Greifswald, the Leibniz Institute of Atmospheric Physics (IAP), and the German Aerospace Center (DLR) Who are we? The IOW is an independent research institute of the Leibniz Association for which
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programming language, e.g. MATLAB, C/C++, Python. Highly motivated and keen on working in an international and interdisciplinary team. Applicants with strong background in the following fields are preferred
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System (W-0046 | all genders) with the freedom to develop and pursue your own research program within a broad scope. As a successful candidate, you will become an integral part of the Multiscale Cloud
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“Research for a life without cancer“ is our mission at the German Cancer Research Center. We investigate how cancer develops, identify cancer risk factors and look for new cancer prevention
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Postdoc (f/m/d): Machine Learning for Materials Modeling / Completed university studies (PhD) in ...
related field # Proficiency in programming languages (Python, C/C++, Julia) # Background in machine learning methods # Experience in developing, training, and tuning machine learning models # Prior exposure