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Preferred Qualifications : Competitive candidates should be highly motivated and possess a PhD in quantitative/spatial ecology of wildlife or a related discipline with a strong quantitative emphasis (ABD
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with R, statistical and spatial analysis Excellent communication and teamwork skills Excellent organizational skills Experience with field work in post-disturbance forest conditions (desired) Experience
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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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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
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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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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 inclusivity and teamwork. - Engage in ongoing academic and intellectual life within CALS and Cornell University. Requirements • PhD in ecology, quantitative ecology, applied statistics, or a
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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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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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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