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on remote sensing of air quality. Emphasis will be placed on using satellite observations to understand the impacts of wildfires and prescribed burning on air quality. The candidatemust have a Ph.D. in a
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for healthcare and dependent care expenses Responsibilities* Integrate survey and administrative datasets with environmental data (e.g., remotely sensed temperature and air pollution) Perform econometric analyses
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. Approval of remote and hybrid work is not guaranteed regardless of work location. For additional information on remote work at Penn State, see Notice to Out of State Applicants . This is a term position
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 18 hours ago
consider one or multiple subtopics that our group is particular interested in and invests on: (1) ice microphysics remote sensing from infrared to microwave spectra (TIR, FIR, sub-mm, MW); (2) cloud
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rolling basis with start dates as early as 1/1/2026 and as late as 3/1/2026. Group or Departmental Website: https://simpsoba.su.domains/ (link is external) How to Submit Application Materials: Please upload
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, NEON airborne remote sensing, comprehensive water balance measurements, soil respiration data, and tower-based remote sensing including multiple thermal imagers and solar-induced fluorescence (SIF
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on remote sensing of air quality. Emphasis will be placed on using satellite observations to understand the impacts of wildfires and prescribed burning on air quality. The candidatemust have a Ph.D. in a
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project and will be genuinely excited to collaborate—both within the lab and with our broader network of partners. They will help strengthen the supportive culture of the lab, which values rigor, humor, and
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, atmospheric signals), data fusion across sensing modalities, and development of scalable machine learning pipelines. Work will be entirely computational and based in Seattle, with no field deployment
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Position Summary This position will focus on integrating high-resolution field monitoring, remote sensing, and statistical and numerical modeling approaches to improve predictive flood hazard