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Field
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creative and interdisciplinary thinking. Strong programming skills in R, Python, Stata and/or SAS, GIS or other relevant software. Excellent oral and written communication skills. Track record of published
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element modeling (preferably COMSOL) is required. The candidate should also have strong computational/programming skills (e.g., Python, MATLAB), and familiarity with geodetic datasets (InSAR, GNSS) and
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, accessibility, and compliance with institutional and regulatory standards. The ideal candidate will have expertise in CAD, Revit, and document control systems, along with a strong technical foundation in GIS
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, remote sensing, machine learning Technical proficiency with quantitative data analytics and workflows (e.g. R, Python, GIS, GitHub) Experience with machine learning, Google Earth Engine, remote sensing
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quality analysis, big data analytics, isotopic analysis Technical proficiency with quantitative data analytics and workflows (e.g. R, Python, GIS, Modflow, GitHub) Experience conducting interdisciplinary
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: hydrogeology and groundwater modeling Technical proficiency with quantitative data analytics and workflows (e.g. R, Python, GIS, Modflow, GitHub) Experience conducting interdisciplinary or community-engaged
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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | 3 months ago
USNH Employees should apply within Workday through the Jobs Hub app As part of the Earth Systems Research Center within Earth, Oceans, and Space, NH GRANIT is the state of New Hampshire’s GIS
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tools from remote sensing, Geographic Information Science (GIS), graph theory, and data science to address complex research questions. Analyzes both aspatial (e.g., tabular) and spatial (vector and raster
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the developed geospatial databases to evaluate trends in stream networks across the US. Work will be done primarily in python and GIS. Candidates will be expected to create maps and graphs of trends in network
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tools from remote sensing, Geographic Information Science (GIS), graph theory, and data science to address complex research questions. Analyzes both aspatial (e.g., tabular) and spatial (vector and raster