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, and validate machine learning and statistical models for health risk prediction; Analyse heterogeneous health-related datasets, including clinical, longitudinal, environmental, demographic
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disease (Braun, Cancer Cell, 2021) in kidney cancer. The ideal candidate will have a strong background in computer/data science (including statistics), knowledge of R and python, and experience with high
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relevant discipline, e.g., environmental statistics, environmental economics, ecology, etc., and demonstrated strength in applied data science with environmental applications. Expertise in R and Python
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Environmental Sciences, Ecology, Climatology or an equivalent discipline, obtained more than one year ago, with previous experience in remote sensing, satellite image analysis, the analysis of spatial trends and
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the Department of Ecology and Evolutionary Biology at the University of Michigan. Research in the lab is focused on the geography of evolution, and particularly on developing statistical methods for spatial
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, Territorial Planning and Management, Economic Geography, and related fields; experience in the construction, development, and interpretation of databases; use of statistical software; analysis of agent networks
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emphasis on emerging wearable optically pumped magnetometer (OPM) technology. These methods and hardware will improve the spatial resolution, robustness, and computational foundations of MEG source imaging
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spatial characteristics of Delaware and Maryland coasts. Possess the background to understand the relationship between physical, biological, and chemical water quality analyses in relation to land use
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Participation Program offers research opportunities to motivated postdoctoral fellows interested in solving agriculture-related problems at a range of spatial and temporal scales, from the genome to the continent
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, glial, and immune cell culture, nucleic acid extractions, ELISAs, bead-based microarrays, immunohistochemistry, spatial transcriptomics, and molecular analyses. The candidate will summarize research data