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Field
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Digital Twin (FLDT) is a flagship research initiative funded by the UF Office of Research that leverages high-performance computing, AI, and geospatial modeling to build AI-powered replicas of the built and
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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 23 hours ago
geospatial data processing and python programming. Candidates should also have knowledge of optical, lidar, and ground penetrating radar sensing systems and understanding of pavement structures and condition
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works with a range of data sources, including administrative microdata, household and firm surveys, and geospatial datasets. The postdoctoral researcher will collaborate closely with the program director
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from the new FireSat constellation) and geospatial analysis. Topic 2 requires a combination of field and remote sensing approaches. For topic 3, experience with processing large geospatial datasets is
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. The selected candidate is expected to have expertise in one or more of the following areas: modeling contaminant flow and transport at various geospatial scales, process-based modeling of soil organic matter
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imagery; ii) Collection and analysis of field data; iii) Land use and land cover studies with a focus on agricultural systems; iv) Organization and management of geospatial databases, preparation
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. Experience with Bayesian methods, graph/network analytics, reinforcement learning, or other advanced AI approaches relevant to industrial systems. Experience with geospatial analysis, spatial data integration
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across heterogeneous study designs in developmental contexts, with familiarity with established reporting frameworks (e.g. PRISMA guidelines). Working knowledge of geospatial datasets (e.g. ERA5, MODIS
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. The selected candidate is expected to have expertise in one or more of the following areas: modeling contaminant flow and transport at various geospatial scales, process-based modeling of soil organic matter
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. The selected candidate is expected to have expertise in one or more of the following areas: modeling contaminant flow and transport at various geospatial scales, process-based modeling of soil organic matter