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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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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
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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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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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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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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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research areas: (1) Environmental Social Science, Education and Communication/Meaning-Making and (2) Environmental Data Science and Spatial Computing. This job listing is for area one, which focuses
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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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to join our team. Our lab focuses on developing and applying innovative statistical machine learning methods, single-cell multi-omics, and systems immunology approaches to investigate immune-mediated