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expertise in cell culture and mouse studies. As the work involves the analysis of spatial transcriptomic data, basic expertise in computational biology, with experience in R and/or Python will be beneficial
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, ecology, and conservation and spans a range of activities from exploratory analysis, visualization, and discovery to prediction, validation, quantification of uncertainty, and inference. To thrive in
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Internal Number: JR91931 Scheduled Hours Empty heading 40 Position Summary Empty heading Performs data management and/or data analysis for investigators using statistical packages. Assists investigators in
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; GIS software; spatial analysis and visualization; programming in R, Python, or similar; quantitative data collection and analysis; scientific synthesis and writing. Experience partnering with non
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starts. Preferably, you will also have: Interest in global water issues and earth system modelling; Strong quantitative methodological skills, for instance knowledge of (spatial) data analysis
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | 2 months ago
of Computational Biology (ICB) , as part of the Computational Health Center at Helmholtz Munich, is globally recognised for innovations in data analysis and modelling of biological systems and diseases, anchored
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modelling in transdisciplinary processes in order to examine ecological and socio-spatial change, existing biodiversity governance regimes and develop multispecies ethnographies to better understand
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areas: GIS software; spatial analysis and visualization; programming in R, Python, or similar; epidemiology; etiology; data communication; systems evaluation; quantitative data collection and analysis
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fish larvae. Although the importance of drift is well established, many of its underlying dynamics—particularly its spatial and temporal variability—remain poorly quantified. This knowledge gap largely
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located at SciLifeLab in Stockholm. Our research is focused on cell biology, spatial proteiomics and machine learning for bioimage analysis. The aim is to understand how human proteins are distributed in