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, Geoscience, Soil Science, Physics, or related fields). Preferred skills: Commitment to contributing to outreach activities and research dissemination. Experience in computer programming (R+, Matlab, Python
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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
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performant solutions to computational problems using Python, Rust, or Julia. Modern frameworks for full-stack web development, including backend (Flask, Node.js), front-end (React, Angular), and/or GIS and
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of guaranteeing thermal comfort throughout all seasons. The project will be developed by integrating parametric urban design with microclimate simulations and measurements, GIS and Digital Twin technologies, and
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science, or a related field; strong experience with GIS and spatial data analysis; experience with Python, or a strong willingness to further develop programming skills; an interest in methodological and
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to maintaining high data quality standards Problem‑solving skills and adaptability to dynamic project needs and field conditions Proficiency with data analysis tools (e.g., R, Python) are considered an asset
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research results. Familiar with statistical analysis applied to GIS data. Working in journalism or in a newsroom and with FOIA requests. Technical Skills or Knowledge: Computing and analysis experience with
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) Requirements: Experience with analyzing GPS tracks Good data-handling skills and ability to use R (compulsary) and preferably also Python and/or GIS competently Statistical/causal inference knowledge PhD degree
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) Demonstrated knowledge and experience in the specialized area of Geographic Information System (GIS). (Required) Demonstrated ability and experience in the integration and analysis of very large data sets from
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biochemical models, data assimilation, spatial analysis and GIS approaches. • Programing skills (e.g. R or Python) for data manipulation and visualisation, and to perform statistical analysis (e.g. mixed models