210 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"NOVA.id" positions at Zintellect
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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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improving plant health using machine learning and artificial intelligence. Mentor(s): The mentor for this opportunity is Yulin Jia (yulin.jia@usda.gov ). If you have questions about the nature of the research
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, Computer Vision, or related field. Application Requirements A complete application consists of: Zintellect Profile Educational and Employment History Essay Questions (goals, experiences, and skills relevant
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projects. Additional opportunities will be the application of artificial intelligence and machine learning approaches to maximize information retrieval from geospatial and temporal datasets. The participant
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and Data Science (including machine learning and AI for defense applications) - Systems Engineering and Engineering Management - Industrial Engineering and Production Management - Mathematical Modeling
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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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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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Organization DEVCOM Army Research Laboratory Reference Code ARL-C-CISD-300144 Description About the Research Current approaches optimize machine learning training largely by exploiting Deep Neural
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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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collection of streaming sensor data. This project focuses on utilizing state-of-the-art reinforcement algorithms to 1) dynamically learn from multi-agent actions and context, 2) evaluate the environment and