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Field
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students and contributing to educational initiatives. Experience with remote sensing, GIS tools, and image analysis techniques is an advantage, as is knowledge of genetic methods (e.g., SNP-based data
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. Proficiency in statistical analysis (R, SAS, etc.) and GIS software (ArcMap). Proficiency in Microsoft Office Suite (Word, Excel, PowerPoint, and Outlook). SALARY: Approx. $4,791.67 Monthly (Approx. $57,500
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hydrologic connectivity metrics. Furthermore, the qualified candidate must possess advanced skills in geocoding, GIS, raster analysis/processing, and the management of large geospatial datasets. Familiarity
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proficiency in languages such as R and Python. Experience in GIS, remote sensing, and processing projected climate data. Proven ability to manage multiple tasks effectively, work collaboratively in team
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Strong organizational and time management skills Strong quantitative data analysis skills Experience using administrative child welfare data Experience and skills in GIS Grant-writing experience Additional
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 months ago
expertise in children’s environmental health, the built environment, disaster epidemiology, pesticide exposure, endocrine disruptors, air pollution, PFAS exposures, and GIS, as well as other areas. Training
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expected to have experience in hydrologic/hydraulic modeling and in one or more of the following topics: in situ sensor installation and flood monitoring, GIS & geospatial big data, AI/ML and data science
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such as GPlates, DAC, GIS for DEM analyses, or related software Some experience in field-based geological methods (e.g., sampling, sediment analysis) or willingness to develop these skills Ability
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reporting skills (4) Experience in spatial data analysis using geographic information systems (GIS) and programming languages (R, Python) as well as experience in numerical model applications and multivariate
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geographic information systems (GIS) and programming languages (e.g. Python, Matlab, R) as well as in advanced statistical methods for analyzing complex ecosystem and environmental datasets. Good knowledge