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-learning algorithms Versatile data-science knowledge, including image and DNA sequences processing Programming skills in Python or other modern programming languages supporting AI and bioinformatics
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The German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE) is a member of the Leibniz Association. The institute’s mission is to conduct experimental and clinical research in the field
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. Remuneration is in accordance with the German public tariff scheme (TV-L Brandenburg), salary group E 13. This is a part-time position with a weekly working time of 30 hours per week. Key responsibilities: PIK
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) and programming languages (e.g. Python, Matlab, R) as well as in advanced statistical methods for analyzing complex ecosystem and environmental datasets. Good knowledge of European marine ecosystems as
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or related fields programming experience in Python or extensive experience in another high-level programming language is a prerequisite experience in geospatial data science, remote sensing, GIS, and open
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or interest in game theory, tax competition and fiscal federalism, welfare economics, environmental economics, public economics is a plus Ideally Working knowledge of programming languages such as Mathematica
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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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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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geographic information systems (GIS) and programming languages (e.g. Python, Matlab, R) as well as in advanced statistical methods for analyzing complex ecosystem and environmental datasets. Good knowledge