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. Excellent analytical skills with high attention to detail. Experience with geospatial data visualization tools such as ArcGIS, Cesium, or similar. Familiarity with urban-scale building energy modeling
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to those with: (a) strong background in quantitative methods, statistics, computer science, geospatial data analysis and modelling; (b) experience in AI and geospatial computer version; (c) advanced
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insights, and applying appropriate AI models for geospatial analysis based on the integrated data. This role is part of the project: Decision-support-tools for Spatial Planning and Cumulative Effects
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make a difference in the world! Position Information Texas A&M AgriLife Research at Temple is seeking a highly motivated scientist with expertise in crop modeling, remote sensing, and geospatial
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and sustainability; Investigate and apply artificial intelligence and machine learning techniques, including large language models (LLMs), across CENSE’s scientific body in its five thematic areas
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technologies, digital soil mapping/pedometrics, proximal/remote sensing, and geospatial modeling. The ideal candidate will work and collaborate closely within a research team at Texas A&M AgriLife and USDA-ARS
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animal movement analyses. Geospatial analysis, GIS, and remote sensing. Statistical, ecological, and machine-learning modeling using modern analytical software (e.g., R or Python). Google Earth Engine and
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equivalent combination of education and/or experience. A minimum of three years of relevant advanced experience in watershed modeling and geospatial analysis. Certifications/Licenses Required Knowledge, Skills
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). 2. Experiencia en modelización forestal y ambiental utilizando datos geoespaciales multifuente. Experience in forest and environmental modeling using multi-source geospatial data. 3. Programación
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assistant will primarily involve preparing and integrating large geospatial datasets, adapting and applying machine-learning models across different forest and environmental conditions, and evaluating model