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on SAR and optical images, GPS, seismometer waveforms, and gravity measurements. Time series analysis of the SAR interferometry and other data is a key part of the research. We then infer the structure and
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responses and microbial persistence. Learn to design, train, and iteratively refine machine learning models to learn distinguishing features of true microbial contaminants versus host-derived and process
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products from airborne and space-borne imaging spectroscopy missions, e.g., the HyspIRI airborne campaigns, DESIS, HISUI, and Hyperion. This effort will also inform the science data processing architecture
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in applying process-based cropping system models to quantify the short- and long-term effects of these conservation practices on soil health and crop productivity for U.S. growers. This specific
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sensing and decision-making in poultry processing environments. Learning Objectives: Under the guidance of a mentor, the participant will gain knowledge and experience in: Applying hyperspectral imaging
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more comprehensive, chromatic picture of stellar activity, focusing on building a sample of well-characterized active stars (high RV jitter) that are amenable to ultra-high signal-to-noise (SNR
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troposphere and stratosphere (UT/S) - and its role in climate. We use a combination of satellite data, high-altitude aircraft measurements, and models to investigate variations in and processes that impact
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Designated Countries will not be accepted at this time, unless they are Legal Permanent Residents of the United States. A complete list of Designated Countries can be found at: https://www.nasa.gov/oiir/export
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. Description: Theoretical research and computer simulation are carried out with emphasis on observations of space plasmas. Specific interest areas include (1) nonlinear phenomena in unstable collisionless
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interest in weather/fuel and related data processing, analysis and visualization, to help research and develop potential expansions to our suite of weather-knowledge-based decision support systems and tools