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Field
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work with existing datasets, including socio-economic, ecological, and geospatial databases that are linked to spatial and environmental data to address a variety of fundamental and applied research
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geospatial data processing and python programming. Candidates should also have knowledge of optical, lidar, and ground penetrating radar sensing systems and understanding of pavement structures and condition
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, crop yield). -Familiarity with geospatial data and tools (e.g., GIS, QGIS, Google Earth Engine). -Knowledge of explainable AI (e.g., SHAP, LIME), model interpretation, and/or uncertainty quantification
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Basic proficiency related to geospatial analysis (e.g., ArcGIS, QGIS, Google Earth Engine). Strong oral and written communication skills. Experience working with and managing interdisciplinary research
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to analyze geospatial data to support resource management decisions for multiple resource, jurisdictional, and ownership units or a large geographical region. It requires knowledge of cartographic principles
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of this research is assessing the fitness of geospatial indicators to inform conceptual and policy-relevant understanding of vulnerability processes for disaster risk reduction and climate adaptation. The researcher
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the Critical Infrastructure Resilience (CIR) Group in the Human Dynamics Section, Geospatial Science and Human Security Division, National Security Sciences Directorate, at Oak Ridge National Laboratory (ORNL
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experience with remote sensing data and its analysis, and geospatial skills Strong statistical background Excellent English writing and verbal communication skills Ability to travel periodically for reports
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 1 month ago
, Javascript, or Matlab. Must have experience in analyzing remotely sensed imagery or other large geospatial datasets. Preferred Qualifications, Competencies, and Experience Experience working in high
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. The selected candidate is expected to have expertise in one or more of the following areas: modeling contaminant flow and transport at various geospatial scales, process-based modeling of soil organic matter