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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 2 months ago
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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platforms for integrating spatial and temporal information, harnessing remote sensing data, using climate information, understanding fuel accumulation and running physics-based or data-driven wildfire
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diversity, pluralism, and individual differences. Required Minimum Education Level PhD Work Location Hybrid — Remote/On-campus Employment Category Fulltime Required Application Documents Cover Letter
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familiarity with model coupling frameworks (e.g., ESMF). Proficiency in programming and data analysis (e.g., Python, Fortran) and handling large datasets, including GIS or remote sensing integration. Strong
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proficiency in languages such as R and Python. Experience in GIS, remote sensing, and processing projected climate data. Proven ability to manage multiple tasks effectively, work collaboratively in team
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techniques to expand and enhance the robust monitoring of cropland carbon budgets. This involves integrating multi-modal data (e.g. remote sensing, eddy-covariance flux tower/chamber measurements, soil
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scholarly excellence and will be expected to work independently. Candidates with experience in other types of satellite remote sensing data, and a desire to use EMIT data and other analytical techniques
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agriculture. Developments in remote sensing, computer science and biogeochemistry support visions of cost-effective and reliable “natural climate solutions”. At the same time, there are hot technical and
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essential to the project. Also, the integration of remote sensing data for analysis with ground-truth data will be useful. The applicant must be able to comply with high standards of work ethics, regulations
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research skills, particularly in statistical modeling, geospatial analysis, and health metrics evaluation. Experience working with a variety of spatial datasets, including remote sensing data, for health and