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well as preliminary research on yield prediction modeling. Learning Objectives: The participant will develop skills in agricultural predictive yield modeling. These will include analysis and interpretation of large UAV
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regions will be evaluated for features such as signatures of selection or diversifying or purifying selection, around genes and regions of agricultural importance. Learning Objectives: The participant will
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phenotyping using both drone-based and ground based sensing platforms. Learn artificial intelligence and machine learning techniques to analyze image and geospatial data from diverse sources for crop monitoring
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Learning about protein design and engineering Exploring cell-based and cell-free screening Applying high-throughput screening Utilizing bioinformatics, machine learning, and other computational approaches
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, the participant will: (1) gain experience in computational modeling to optimize manufacturing high-purity reactive refractory metal powders, (2) learn advanced modeling methods based on density functional theory
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-property relationships, statistics and probability, applied mathematics, data science, or machine learning. Application Requirements A complete application consists of: Zintellect Profile Educational and
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Raman imaging technologies for safety and quality evaluation of agricultural products. Learn artificial intelligence/machine learning methods to evaluate hyperspectral image data to assess safety and
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computing facilities at the DOD Supercomputing Research Center. What will I be doing? This project will focus on learning, adapting, and applying US Army Corps of Engineers-developed or supported coastal
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to the continent, and sub-daily to evolutionary time scales. One of the goals of the SCINet Initiative is to develop and apply new technologies, including artificial intelligence (AI) and machine learning, to help
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and stress), behavior (grazing behavior halters, accelerometer-based ear tags, chute velocity measures), environmental monitoring, and pasture measures. Learning Objectives: Under the mentor's guidance