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Field
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research program focuses on developing innovative cell-based therapies and model systems for CNS diseases, including Alzheimer's disease, Parkinson's disease, multiple sclerosis, and brain cancers. We
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following qualifications and experiences: Experience with developing or applying ecosystem models (e.g., SWAT-Carbon, CLM, EPIC, DNDC, and DayCent) Experience with analysis of geospatial data and time series
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. This appointment will be for two years in the first instance with consideration for extension. We investigate tumors as an ecosystem in which microbes (viruses, bacteria, etc) synergize with genomic alterations
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publicly available data from an array of cities. As such, the postdoc should have strong experience working with large datasets, modeling ecological data, and maintaining national/international
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the Western United States. The candidate will contribute to innovative research quantifying the risk of losing key forest ecosystem services (e.g., timber production, carbon storage, recreation, soil erosion
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an industry partnered project for translational drug discovery. The role will involve analysing large scale omics and spatial datasets from both primary patient samples and advanced in vitro model systems
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. Our work supports the health assessment of marine mammal populations and informs conservation efforts for the North Sea ecosystem. Your research will cover key topics such as disease, reproductive
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an industry partnered project for translational drug discovery. The role will involve analysing large scale omics and spatial datasets from both primary patient samples and advanced in vitro model systems
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 days ago
) ecosystem a cloud-based platform that provides secure access to biomedical data, analytic tools, and collaborative workspaces. This position offers the opportunity to work on cutting-edge initiatives aimed
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the natural sciences. The position offers access to top-tier academic and industry research ecosystems and is ideally suited for researchers seeking to push the boundaries of geometric and graph-based learning