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. Additional Desired Qualifications: Demonstrated success in collaborative environments. Experience with NASA remote sensing data and machine learning. Experience communicating scientific concepts to wide
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the shallow subsurface (<10 meters depth). Experience with soil moisture/salinity and sapflow sensors. Experience using neural networks and machine learning tools. Stipend $70,000.00 – $80,000.00 Yearly Point
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gallium nitride MEMS technology. In this specific project the postdoc will help with developing a thermal infrared imaging micro-instruments working at 500C using GaN based acoustic and micromechanical
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, high-efficiency transmitters, low-phase noise RF sources, and other critical radar components. Topics also cover radar signal processing and machine learning, applying advanced techniques to enhance
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such as VOC vapor intrusion or groundwater and drinking water concerns in southern California or in Guam. Through this project, you will have the opportunity to learn ATSDR’s approach to conducting
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missions (e.g., Surface Biology and Geology - SBG). This could involve advancing atmospheric correction, dimensionality reduction, or machine learning approaches for handling big data in order to improve
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primary learning experiences. You will examine the susceptibility of table grape breeding lines to gray mold caused by Botrytis cinerea, including developing and conducting scalable, high-throughput
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efficiency, productivity, and crop yield. Additional learning activities may include examining salt-tolerant crops capable of using lower-quality groundwater. Participation in these research activities will
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. Learning Objectives: Under the guidance of a mentor, you will have opportunities to: Advance science communication skills through preparation of scientific manuscripts and presentation of research results
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problems that affect Americans every day from field to table. Learning Objectives: Under guidance of a mentor, the participant will: Learn fundamental and applied concepts in grapevine physiology, molecular