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Postdoctoral Fellow Advancing Nature-Based Carbon Accounting in Eastern US Forests via Machine Learning and Remote Sensing O’Neill School of Public and Environmental Affairs Bloomington, Indiana Indiana
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Sciences at Syracuse University seeks applicants for a Postdoctoral Researcher position in snowmelt detection using satellite remote sensing. This is a fully funded, full-time, 1-year position (renewable
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-based, remote sensing and numerical model simulated data, the successful candidate will lead research on 1) developing new remote sensing theories and algorithms to monitor regional to global vegetation
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of good quality remote sensing data. • Perform hydrodynamics simulations using numerical models for vast coastal area. • Develop original software for data fusion and output visualization
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Arizona State University, School of Earth and Space Exploration | Tempe, Arizona | United States | 10 days ago
that scales from the microsite to the North American dryland region by joining field, remote sensing, and a hierarchical continuum of models in a spatially-nested approach. The project will develop a process
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of these species. Specifically, the postdoc will use extensive tracking data and high-resolution remote sensing datasets to study how habitat selection at non-breeding grounds affects spring phenology and breeding
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change mitigation in tree-based systems; and deploy models using remote sensing data and machine learning algorithms to map tree carbon in Africa and Asia. This is an end-dated, grant-funded, benefits
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space-time data, e.g. from remote sensing of the Earth. Traditional prediction methods are not suitable for such data, mainly due to high computational complexity as well as other limitations [1]. Recent
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: RSnowAUT A modular remote sensing pipeline incl. ground-truth monitoring for automated snow avalanche detection and forecasting Project duration: 2022 – 2025 Development of a data pipeline that can process
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, NO2, SO2, O3, and VOCs), daily, air quality dataset across the Marcellus and Utica shale regions from 2004 to 2023 at a resolution of 1 km using a combination of remote sensing, ground measurements, and