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. Qualifications The ideal candidate will have a master's degree in a related scientific field. Knowledge with satellite-based remote sensing data, satellite-derived bathymetry inversions, data processing, and
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related area, including meteorology, hydrometeorology, remote sensing, surface and atmospheric modeling, or related fields. Experience in machine learning techniques are highly desirable. Please see https
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. Specifically, the aims are to develop algorithms to retrieve from PACE, GLIMR, and/or SBG remote sensing reflectance, (1) ocean phytoplankton community composition targeting the major taxonomic groups, (2
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, Crop Science, Weed Science, Agricultural Engineering, Remote Sensing, Computer Science, or a closely related discipline). Degree must have been received within the past five years or is anticipated to be
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ground. Here the atmosphere is to be characterized as a complex random medium with spatial-temporal fluctuations. Advanced computational techniques are needed. (2) Acoustic remote sensing
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into the solar wind. This property provides a valuable means to connect coronal plasma signatures to in situ solar wind measurements. The project may also involve comparing these remote-sensing observations with
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of fellowship appointment. Questions about this opportunity? Please email npp@orau.org Qualifications Strong background in remote sensing, satellite data analysis, and/or machine learning methods. Familiarity
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ground. Here the atmosphere is to be characterized as a complex random medium with spatial-temporal fluctuations. Advanced computational techniques are needed. (2) Acoustic remote sensing