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tools and machine learning for advanced image analysis, weed-crop detection, and mapping. Experience in data collection, processing, and interpretation. Strong background in precision agriculture and
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Sensing Nuclear Science and Weapon Effects Artificial Intelligence, Machine Learning, and Cyber Security Materials, Extreme Environments, and Optical Sciences Remote Sensing and Radiation Detection
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opportunity of the fellows will be to participate in the preparation of samples for chemical analysis and learn a range of skills associated with a chemistry laboratory. Samples may be soils, sediments, plant
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spectroradiometers. Ability to apply AI tools and machine learning for advanced image analysis, weed-crop detection, and mapping. Experience in data collection, processing, and interpretation. Strong background in
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to develop novel statistical techniques, analyze satellite and other remote sensing data, implement machine learning algorithms, assess numerical model performance, improve risk assessment tools, and deepen
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the growth of America's scientific leadership in emerging fields, fostering the research necessary to maintain global competitiveness in innovative technologies. The learning objectives for this project
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, students will: Learn from and collaborate with scientists and engineers at DoD facilities across the nation. Contribute to significant Research, Development, Test, Evaluation & Acquisitions Engineering
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experience with time-series data analysis and machine learning including reinforcement learning. Applicants should be proficient in Matlab and/or Python Point of Contact ARL-RAP Eligibility Requirements
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pathway for undergraduate students. EQuIPT is a 10-week, full-time, student-focused internship. Under the guidance of a mentor, you will learn and gain experience engaging with LQC researchers, industry
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of ARS National Programs 305 (Crop Production) and 304 (Crop Protection & Quarantine). The successful candidate will learn about project management by being a part of research aimed at identifying