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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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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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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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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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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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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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. This is an entry level position for a candidate who has recently completed a PhD in transportation engineering or planning, air pollution modeling, health impact and risk assessment of air pollution and/or
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have expertise in geometallurgy, applied geochemistry, and geo-environmental assessment, with advanced proficiency in integrating spatial data into 3D models using sophisticated interpolation techniques
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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