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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 13 hours ago
/or machine learning/artificial intelligence algorithms. Projects may also include work focused on the analysis of spatial and geographic data and work extrapolating results to different spatial scales
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for collecting data on environmental and health parameters (air quality, noise, mobility). GIS Proficiency: Proficiency in Geographic Information Systems (GIS) to map and analyze spatial determinants of urban
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candidate will contribute to projects involving participants with implanted intracranial electrodes and wearable non-invasive sensors, with a focus on memory consolidation, spatial navigation, and neural
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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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advanced spatial statistics in R and QGIS is required. The ability to work both independently and collaboratively in a team environment is essential. Pay and Benefits Pay Range: $62,000 - $65,000; depending
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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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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