87 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"L2CM" positions at Leibniz in Germany
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As the German National Library of Science and Technology, our future-oriented services ensure the infrastructural requirements for a high-quality supply of information and literature for research in
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responsibilities: support our research on the political economy of climate policies take part in data science and empirical analyses contribute to assessments of cause-effect-relationships in the domain of climate
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the institute, and a dedicated, powerful bioinformatics server infrastructure has been established for data analysis. We seek an enthusiastic scientist with interests in bioinformatics, microbiology, and
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), biostatistics, machine learning, data science and research data management, and causal inference methods (Iris Pigeot, Marvin Wright, Vanessa Didelez), and etiologic and molecular epidemiology (Konrad Stopsack
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are looking for a full-time position with the option to be based either at our main location in Kiel or our Research Hub in Berlin. The position is designed for individuals passionate about data analysis and
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and other stakeholders - knowledge-driven and application-inspired. To strengthen the Department Data Science in Bioeconomy we are offering the following position to be filled on 1st March 2026
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(Administration) recruiting.leibniz-lsb(at)tum.de . We look forward to receiving your application! Data Protection Notice: As part of your application for a position at Leibniz-LSB@TUM, you will transmit personal
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development for lidar control. Data evaluation is an essential part of the project work to distinguish between natural variability of the observed metals and aerosols and anthropogenic effects. Your
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on in-situ hybridization, in–vivo electrophysiology data analysis with mice and is tightly linked to independent subprojects involving in-vivo electrophysiology in humans and high-resolution fMRI in awake
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differing spatial resolutions. Using catchment-scale ecological and water quality data, the project aims to assess how abiotic drivers, biotic interactions, and stochastic processes jointly shape species