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opportunity is with the DCPH-A Toxicology Directorate that provides toxicological assessment of novel military-relevant compounds (MRC) currently under research, development, testing, and evaluation (RDT&E
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on leveraging data from permanent forest plot datasets in the Caribbean to build a comprehensive view of tropical tree species in support of conservation and broader management actions. Learning Objectives
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education and support your academic and professional goals. Along the way, you will engage in activities and research in several areas. These include, but are not limited to, Learn how to communicate data and
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parameter space and interdependence of different variables that affect the desired performance. Artificial intelligence and machine learning models have demonstrated the potential to improve the effectiveness
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because of the large parameter space and interdependence of different variables that affect the desired performance. Artificial intelligence and machine learning models have demonstrated the potential
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design, and hands-on learning. Educators will also engage in ORNL’s Traveling Science Fair, gaining deeper insight into how scientific concepts are applied in real-world scenarios and how national
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design, and hands-on learning. Educators will also engage in ORNL’s Traveling Science Fair, gaining deeper insight into how scientific concepts are applied in real-world scenarios and how national
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wildland-urban interfaces— across a wide range of climate conditions. Using machine learning methods, we will optimize the weightings of each contributing factor and identify the key drivers of wildfire risk
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medical treatment facility. One of the most exciting and unique components of DGMC is the Air Force Clinical Investigation Facility. At the CIF, you will have the opportunity to participate in cutting edge
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and prepare for readiness during operational tasks. The team is further exploring novel AI method developments, including applied mathematical and machine learning solutions for real-time use. Why