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October 2026 Apply now Do you want to use artificial intelligence, geospatial data science, and urban analytics to create healthier and more equitable cities? Are you excited about developing innovative AI
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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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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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datasets and remote sensing products (e.g., satellite-derived indices, geospatial data analysis) is highly desirable. The selected candidate will also provide support (30%) to a second project focusing of
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) Evaluate the implications of these associations for extraction processes; and 3) Assess the critical mineral resource potential of unconventional wastes through geospatial and statistical analysis of supply
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field. Demonstrated experience with satellite remote sensing for large-scale vegetation or forest monitoring, including Landsat and/or Sentinel-1/2 time series. Strong proficiency in Python for geospatial
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Jersey. This project integrates residential histories, geospatial air-pollution estimates, cancer registry data, medical and pharmacy records, patient-reported outcomes, and circulating biomarkers
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forest trees would be an advantage. International research environment The position benefits from the continuing RESDiNET network, connecting IFE SAS with the Finnish Geospatial Research Institute
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the structure of real prospection activities by integrating multiple, heterogeneous geospatial and archaeological data layers. Within this environment, we will design and evaluate new reinforcement learning
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reinforcement learning. The project will investigate a simulation platform that reproduces the structure of real prospection activities by integrating multiple, heterogeneous geospatial and archaeological data