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Field
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modelling, geospatial data science, generative modelling, mobility data analysis, or transport simulation, especially when combined with knowledge of complex systems, network science, resilience theory, urban
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Collaborate with Dr. Stuart Jones, AWRI-based researchers, and other international scientists Manage large geospatial datasets Develop, use, and refine process-based models of lake physics and biogeochemistry
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. Experience with spatial analysis and geospatial data integration, including use of GIS tools (e.g., ArcGIS, QGIS) to link, harmonize, and analyze datasets across spatial scales. All positions are security
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Summary The Computing and Robotic Construction Laboratory at the Department of Civil, Environmental, and Geospatial Engineering, Michigan Technological University invites applications for a
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designing and conducting interviews and surveys, managing large secondary datasets, and building and managing geospatial datasets. Minimum Requirements for the rank of Postdoctoral Scholar: • Ph.D. in a field
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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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. Working with geospatial and mobility datasets (GPS trajectories, transit feeds, sensor data, demographic/socioeconomic data) Co-designing tools and analyses with municipal and MPO clients, including
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on reproducibility and open-source best practices. Demonstrated experience in geospatial data analysis and the management of large, gridded meteorological or environmental datasets (e.g., NetCDF
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the team with compiling and analyzing geospatial datasets, developing and testing deep learning algorithms for complex image classification, optimizing data collection strategies for unmanned aerial vehicle
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
emphasis on remote sensing. 2. Experience of using multiple sources of remotely sensed data, particularly optical, Lidar, and Radar data. 3. Sound statistical skills and use of Machine Learning/Geospatial AI