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and dry forest-grassland ecosystems. We seek candidates with experience in integrating multi-source remote sensing with field sampling to study vegetation dynamics (e.g., phenology) and its response
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field (e.g. Environmental Engineering, Ecology, etc). We are particularly interested in applicants who have experience with satellite remote sensing (optical and/or radar) and background in surface water
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interested in applicants that have experience in one or more of the following areas: satellite remote sensing, energy balance modeling, and machine learning. In addition to scientific expertise, the successful
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to advance the application of computational hemodynamic models of cardiovascular flow to aid in remote tracking and early diagnosis of disease. The applicant will gain exposure to other projects in the lab