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Field
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statistical and geospatial software (R, ArcGIS, QGIS) and atmospheric dispersion modeling (AERMOD, WindTrax, CALPUFF) is strongly preferred. Ability to multi-task and work cooperatively with others. Why Work
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 24 hours ago
ability to program (e.g, python, R, and/or javascript). Must have at least some experience working with geospatial data. Preferred Qualifications, Competencies, and Experience Ability to develop impactful
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interactions, climate modeling, hydrology, geochemistry, oceanography, active tectonics, surface processes & Quaternary studies, geochronology, sedimentology, regional geology, and geospatial analytics applied
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to peer-reviewed scientific publications. Comfort with Google Earth Engine, geospatial data formats, or Earth observation integration. Ph.D. in Computer Science. Pay and Benefits Pay Range: $110,240
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/Ability 1: Experience running statistical models in SAS. Required Skill/Ability 2: Experience in utilizing ArcGIS software for geospatial analysis. Required Skill/Ability 3: Excellent organizational and
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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | 3 months ago
already incorporated at 400m resolution, to export and edit data to ensure grids to meet 100 m resolution requirements. Position may include work with GIS, Excel and geospatial metadata, as
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area two, which focuses on integration of advanced data science with geospatial technologies to model environmental systems and predict climate impacts. Successful candidates will contribute
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of geospatial data building, modeling, or analysis, including advanced spatial analysis, data manipulation, or cartography software. Knowledge of the Dallas-Ft. Worth economy, including employer and workforce
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geodatabases and spatial data, including GIS software (e.g., ArcGIS, QGIS), for organizing, analyzing, and visualizing geospatial datasets relevant to agricultural research. Ability to work effectively both
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Skills: Strong analytical skills and demonstrated experience with statistical programing in R. Experience using geospatial data for landscape-scale research. Experience with Species Distribution Modelling