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-based, remote sensing and numerical model simulated data, the successful candidate will lead research on 1) developing new remote sensing theories and algorithms to monitor regional to global vegetation
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([email protected]). Minimum Education and Experience: The candidate must have a Ph.D. in a related field (e.g., Atmospheric Science, Earth Science, Environmental Science, Environmental Engineering, Remote
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Knowledge, Skills, and Abilities Ability to program/deploy/retrieve remote sensing units as needed for various research projects. Familiarity with MSQL, R, MS Office, and advanced Excel functions. Experience
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of Ecosystem Dynamics and Remote Sensing is seeking a highly motivated Postdoctoral Associate to lead research in modeling vegetation photosynthetic activities using space-borne LiDAR data. The successful
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Knowledge of plant breeding, plant genetics, plant pathology, remote sensing, high through-put phenotyping, entomology, soil science, weed science, advanced statistics, molecular biology, and plant genetics
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to alleviate stresses. Knowledge of remote sensing, drones and/or digital image analysis preferred, but not required. Equipment Utilized Word, Excel, SAS (or other statistical analysis software), OneDrive
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Knowledge of plant breeding, plant genetics, plant pathology, remote sensing, high through-put phenotyping, entomology, soil science, weed science, advanced statistics, molecular biology, and plant genetics