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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 1 hour ago
. Integrate independent references for rigorous validation. Methods & Data Engineering Design generalizable, well-documented pipelines for data fusion and modeling using established geospatial and scientific
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engineering, computer science, data science, geospatial statistics, or related discipline. Required Knowledge, Skills and Abilities: Familiarity with remotely sensed geospatial data products (UAS and satellite
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qualifications: Experience creating informative web-based data visualization, especially with geospatial data (e.g. maps). Successful candidates will be expected to lead and collaborate on research projects full
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assessment, and geospatial modeling methods and tools. Applicants should have strong, demonstrated research ability, and excellent English written and spoken communication skills. Preference will be given
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 1 hour ago
across diverse landscapes. To achieve this, we will first create a comprehensive, multi-modal data repository by integrating diverse streams of satellite, meteorological, and geospatial data, which
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models for each study site, informed by sensor networks and image-based observations. Conduct cross-disciplinary geospatial analyses linking modeled flooding patterns with soil pathogen and participant
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and mathematical modeling, hierarchical statistical modeling, machine learning, remote sensing, geospatial statistics) • Demonstrated ability to conduct independent research and publish high-quality
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Earth scientists, geospatial experts, and computational scientists to leverage leadership-class computing resources for large-scale model training, testing, and deployment. Knowledge Dissemination
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• Demonstrated experience in computational or quantitative research methods. • Strong programming skills in Python. • Experience with high-performance computing, geospatial data, and causal inference methods
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(e.g., CWT, PMF). Strong quantitative and data analysis skills, including proficiency in handling large environmental datasets. Proficiency in geospatial analysis Knowledge of contaminant fate and