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engagement, and work collaboratively to useR scientific capabilities across ORNL. Collaborate with data scientists, machine learning scientists, remote sensing scientists, HPC engineers, Energy grid subject
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information science, remote sensing, environmental engineering, natural sciences, or related field with two (2) years of relevant work experience. An equivalent combination of education and experience may be considered
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of climate finance, climate policy, resilient development, and climate risk analytics. Experience analyzing big data, ensemble simulations, and remotely sensed data products. Efficiency in the use of high
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Qualifications: Experience in surface hydrologic, hydraulic, and reservoir management modeling Experience in using remote sensing data, statistics, spatial analysis, visualization and data management Experience
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economy at risk. The GSHS Division is comprised of multi-disciplinary scientists and engineers with expertise in the fields of human dynamics, remote sensing, geoinformatics engineering, and geographic data
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data applications, primarily with remote sensing sensors. The proposed solutions will include GPU-based HPC solutions that can be applicable for several geospatially focused research and development
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about different cultures and cultural practices in the U.S. and around the world. Experience in analyzing and interacting with social media, digital trace data, or time series data. Experience with remote
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scientists, remote sensing scientists, HPC engineers, and geographers to deliver research and development prototypes. Deliver ORNL’s mission by aligning behaviors, priorities, and interactions with our core
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on remote sensing and image analysis. Under the guidance of research staff, the selected applicant(s) will take roles on multidisciplinary teams supporting cutting-edge research and engineering with large
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on remote sensing and image analysis. Under the guidance of research staff, the selected applicant(s) will take roles on multidisciplinary teams supporting cutting-edge research and engineering with large