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continental forest datasets (e.g., ForestGEO, NEON, FIA) with remotely sensed datasets (including Lidar and satellite-derived datasets). This position is part of a federally-funded project examining impacts
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of climate change. The position is geared for a recent PhD graduate with interest in collaborating across disciplines, and expertise in remote sensing, spatial data analysis, machine learning and computer
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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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Qualifications Prior experience with learning technology Prior experience working at the intersection of Artificial Intelligence and Learning Science Prior experience with multimodal sensing (e.g., eye tracking
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decoding of electrical activity in deep brain structures during abnormal movement in Parkinson’s disease patients was performed using novel and investigative sensing neurostimulators. Our team has