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Field
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use Finland-wide remote sensing resources from one of the consortium members, Finnish Geospatial Research Institute to monitor tree growth in Finland and combine this information with whole genome
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area two, which focuses on integration of advanced data science with geospatial technologies to model environmental systems and predict climate impacts. Successful candidates will contribute
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 days ago
ability to program (e.g, python, R, and/or javascript). Must have at least some experience working with geospatial data. Preferred Qualifications, Competencies, and Experience Ability to develop impactful
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National Aeronautics and Space Administration (NASA) | Fields Landing, California | United States | 3 months ago
the combination of socioeconomic data and geospatial information. Experience with the calibration and deployment of low-cost sensors is preferred. Occasional travel may be necessary. Field of Science: Earth
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historical CORONA satellite imagery Integrate multi-source datasets including GEDI LiDAR and GLOBE citizen science observations Apply cutting-edge geospatial and statistical modeling techniques to quantify
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, remote sensing data analysis, images processing, and geospatial analysis. Experience working with satellite imageries (e.g., Sentinel-1, Landsat, Sentinel-2) and other remote sensing data sources
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. The post-doctoral research associate will be a key member of the Center of Geospatial Intelligence and Environmental Security research team and will be involved in ecological and agroecosystem change
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geospatial modelling of agricultural management practices across Canada, particularly those related to fertilizer use and cropping practices. As part of this, the postdoctoral researcher will contribute
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following topics: in situ sensor installation and flood monitoring, GIS & geospatial big data, AI/ML and data science approaches for hydrologic predictions, risk analysis. Experience in working with big data
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techniques. Proficiency in R, Python, or MATLAB for data processing, geospatial analysis, and statistical modeling. Experience with time series analysis, spatial mapping, and oceanographic data interpretation