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Field
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Computer Science Department at Princeton University. We seek candidates with computational biology, bioinformatics, computer science, machine learning, statistics, data science, applied math and/or other
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of biotic heterogeneity in urban environments and the spatial/temporal dynamics of these drivers. This will involve working with pre-existing data collected by the lab, as well as collaborator data and
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for improving the environmental impacts of wood production over broad spatial scales. Required Skills and Qualifications: PhD in a forestry- or conservation-related discipline. Strong background in field survey
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of the full-chain between transportation and public health outcomes including traffic and people’s mobility, air pollution, spatial assignment of exposures, human exposures, and public health impacts, in
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of biotic heterogeneity in urban environments and the spatial/temporal dynamics of these drivers. This will involve working with pre-existing data collected by the lab, as well as collaborator data and
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Computer Science Department at Princeton University. We seek candidates with computational biology, bioinformatics, computer science, machine learning, statistics, data science, applied math and/or other
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LiDAR and UAV photogrammetry) with physiological and spectral indicators of forest health. The research will be conducted at multiple spatial scales, from single trees to landscapes, and integrated
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they would design, organize, and conduct advanced quantitative (statistical, spatial, etc.) analyses relevant to climate equity, environmental justice, and/or environmental health; translate complex data and
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or closely related sciences*; Knowledge about spatial modeling, remote sensing, and geographic information systems; Knowledge and expertise in spatial modeling using satellite or airborne images and airborne
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disorders. The successful candidate will apply cutting-edge techniques in spatial and single-cell transcriptomics to study the effects of gene knockouts across multiple scales, contributing to a comprehensive