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areas. These include, but are not limited to: Applying machine learning algorithms to solve real-world problems. Creating and structuring databases for storage, retrieval, and image analysis. Determining
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, economic evaluation, and surveillance to learn how to operationalize, and when needed, improve analytical tools. Research activities will support rapid risk assessment, emergency preparedness, and
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the relative importance of processes in simulating the CMRB agroecosystem. Learning Objectives: Under the guidance of the mentor, the participant will: Learn how cropping system design must align with local soil
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measurements, airborne measurements from field campaigns (e.g., NASA CAMP2EX, ARCSIX) or surface observations. Research involving machine learning techniques will be strongly encouraged. Field of Science: Earth
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at https://ntl.bts.gov/ntl. Are you interested in learning how to develop a new archival repository into a more discoverable, rich resource for the Department of Transportation? Here is an opportunity
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. Research Project: Joining a team of landscape and fire ecologists to learn about and support geospatial analysis of fire mitigation implementation in the highly fire prone lands of Kona and Kohala on Hawaii
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candidates who would like to learn and gain experience in how to plan and perform large animal research and/or burn research in a military medical environment. What will I be doing? As an ORISE participant
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project), create a unique opportunity to apply machine learning and neural network methodologies, in conjunction with simplified ice sheet models, to advance understanding of ice sheet basal processes and
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high school seniors, who are pursuing undergraduate studies in STEM, with an opportunity to explore the world of agricultural science through hands-on learning experiences. Participants will have the
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and interpreting results. Learning Objectives: Under the guidance of a mentor, you will be given the opportunity to participate in research aimed at protecting United States agriculture, timber and