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ExperienceNone Additional Information Eligibility criteria The postdoc should have a PhD degree in evolutionary biology, with expertise in bioinformatics, statistics, programming and/or modeling. Previous
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data analysis, the use of computational, statistical, and data scientific approaches for interdisciplinary research, and data integration involving both spatial and non-spatial data. We expect candidates
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/southern Sweden and potentially other locations (> 8 hours drive from Umeå). Other meriting qualifications are: Strong quantitative skills, with experience in statistical modeling or spatial data analysis in
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) at the Dana Farber Cancer Institute (DFCI) seeks a highly motivated Scientist II or Senior Scientist to support advanced analytics and data integration efforts across imaging, spatial proteomics, and high
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to UC’s success. Job Overview Applications are invited for a tenure-track Assistant Professor position in statistics within the Department of Mathematical Sciences, which offers BS, MS and PhD degrees in
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; Experience with (statistical) data analysis; Previous experience with agent-based modeling or any type of spatial modeling is beneficial; Ability to work with spatial data/GIS is an asset; Domain knowledge in
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» Applied mathematics Mathematics » Statistics Technology » Energy technology Researcher Profile First Stage Researcher (R1) Country Switzerland Application Deadline 11 Jan 2026 - 22:59 (UTC) Type of Contract
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). The institute also maintains locations in Dedelow and Paulinenaue. Within the DFG-funded project “MEGAFORP – Me chanisms of g ene flow a mong spatially isolated for est herb p opulations in European agricultural
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in their decisions and businesses in their strategies. Do you want to know more about LIST? Check our website: https://www.list.lu/ The Luxembourg Institute of Science and Technology (LIST) is a
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Qualifications PhD in Computational Biology, Bioinformatics, or a related field (e.g. statistics, computer science, or quantitative biology). Experience in the application and development of computational methods