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Field
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. Experience with GIS data analysis, data cleaning/curation, and integration of multiple data formats (including CAD). Working knowledge of SQL and relational databases for GIS workflows. Experience with Python
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, and other information using Python (including numpy) and GIS software. Publish codebooks and datasets to allow public access to analysis, and to drive legal and academic scholarship. Coordinate with
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(GIS)and management of raster formats (NetCDF or GeoTIFF). Programming (Python, R or similar) for data processing and process automation. Other requirements to consider: Promoting teamwork and
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: Geographic Information Systems (GIS) and management of raster formats (NetCDF or GeoTIFF). Statistical models (time series analysis, probability distributions). Programming(Python, R or similar) for data
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and geographic information systems (GIS). Familiar with DevOps practices and version control systems (Git). Comfortable with shell scripting (bash) and markup languages (YML, XML, HTML). 5+ years
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) that represents entities such as people, projects, grants, publications, places, and outputs—and models their relationships over time. The position will build robust Python-based ETL/ELT pipelines, implement SQL
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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 1 month ago
, there may also be opportunities to contribute to MIT courses on urban design, GIS, data visualization, and the relationship between technology (including AI) and the urban environment. A full job
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currently enrolled in a GIS degree or certificate program is preferred. Minimally requires a Master's degree. ARL is an authorized DoD SkillBridge partner and welcomes all transitioning military members
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their acquisition, processing, and analysis, knowledge of environmental and spatial data formats (including netCDF, GeoTIFF), proficiency in GIS tools (ArcGIS, QGIS), knowledge of statistical methods and time series
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disadvantaged under county‑level payment rules. Your role will involve: Extracting and processing geospatial data from CDL, Sentinel‑2 L2A, NASS QuickStats, NLCD, and Kentucky GIS portals Computing vegetation