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Landscape analysis and modeling, with a focus on training and application of predictive machine learning tools. This researcher will synthesize regional-scale remote sensing imagery and ecological monitoring
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Requisition Id 17092 Overview: The Environmental Sciences Division at Oak Ridge National Laboratory (ORNL) is seeking a Postdoctoral Research Associate to join the Earth Systems Modeling Group (ESMG
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and exploring technological advancements in the field. Background and Objectives As part of the project titled “Development of an Intelligent Geospatial Interface for Panel 8 of the Benguerir Phosphate
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intelligence. Our research spans computer vision, machine learning, and natural language processing, focusing on multimodal learning, data fusion, spatial-temporal modeling, and vision–language models. We study
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, environmental modelling and geospatial data science. The position is part of Vertical Africa (VERTICAF), an interdisciplinary research project funded by the Swiss National Science Foundation (SNSF). We
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 16 days ago
inform representations and encoder adaptations. This activity will provide hands-on experience in making three-dimensional vegetation structure accessible within data-driven geospatial modeling processes
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start date. Preferred Qualifications: Research experience in computer vision, NLP, geospatial data systems, or high-performance computing. Brief Description of Duties: The Postdoctoral Associate will
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of large-scale geospatial and time-series datasets. The candidate will develop and evaluate multimodal AI models to characterize vegetation and land-surface dynamics and quantify ecosystem responses and
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October 2026 Apply now Do you want to use artificial intelligence, geospatial data science, and urban analytics to create healthier and more equitable cities? Are you excited about developing innovative AI
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. The candidate will contribute to projects focused on developing advanced machine learning models to quantify phenotypic and agronomic traits of crops, including corn, soybean, and other selected species