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Field
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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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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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temperature. The successful applicant will Analyze imagery from NASA ECOSTRESS instrument on International Space Station for retrieval of surface infrared radiances and sea surface temperature at 70 m spatial
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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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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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conduct advanced quantitative (statistical, spatial, etc.) analyses relevant to climate equity, environmental justice, and/or environmental health; translate complex data and analyses for mainstream
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to offer. Qualifications: Required: PhD in ecology by start date Experience in plant phenology, biogeography, and spatial and temporal modeling (Bayesian and frequentist) Expertise in R or Python, GIS, big
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applications from prospective postdoctoral scholars. Potential projects involve investigating the neural mechanisms underlying age-related changes in spatial navigation and memory. Methods to be used include
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As a fellow you will join our faculty in the Department of Biostatistics, providing statistical support and developing innovative biostatistical methods for research projects at the cutting edge