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Field
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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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, Geospatial Science, Data Science, Computer Engineering, or a closely related field, with an emphasis on machine learning, AI, remote sensing, computer vision, or interdisciplinary data science applications
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for spatio-temporal data. Advanced Python skills and experience with ML frameworks and geospatial tools (e.g., PyTorch/TensorFlow, rasterio/GDAL). Ability to work independently and produce reproducible
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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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, Agronomy, modeling, biostatistics, or related field The applicant should have documented knowledges in Geospatial analysis, machine learning, and predictive modelling, Have a good command of programming