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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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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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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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, machine learning scientists, remote sensing scientists, HPC engineers, earth and planetary scientists, and geographers to deliver research and development prototypes. Deliver ORNL’s mission by aligning
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technologies Field, laboratory, or numerical testing of hydraulic structures Geospatial and remote sensing data analysis (e.g., GIS, Google Earth Engine) Machine Learning applications in hydrology/water