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gain valuable knowledge and skills in training machine learning models and applying Natural Language Processing (NLP) techniques such as Topic Modeling and Named Entity Recognition. Additionally, you
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) framework to assess biological risks associated with blast overpressure (BOP) from military weapon systems. You will engage in research and applied computational activities to model blast-induced energy
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-on experience in infectious disease modeling and vaccinology in an applied government setting while learning and practicing Bayesian methods for nowcasting, forecasting, and Rt estimation. You will also develop
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to identify key uncertainties in estimating direct data center water use, including development of reproducible data extraction and documentation approaches; and Develop and evaluate a regional machine learning
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scales, from the genome 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 AI and machine learning
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of unknown PFAS, supporting agricultural research and advancing the ARS mission. The project will involve compiling high-resolution mass spectrometry (HRMS) databases for PFAS, creating machine learning models
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information technology (IT) security to model the dynamic physical consequences of cyber-attacks on the nation’s most critical infrastructure—including power grids, water systems, natural gas pipelines, and other
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of tropical fruit, vegetable and ornamental crops grown in the Pacific Basin.. During this fellowship you will engage with research to extend existing computer models of surveillance traps for invasive insects
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applying customized Bayesian statistical models for wildlife management and monitoring data. Learning how spatial and ecological modeling approaches are used to study wildlife disease spread and management
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of Yuhua Duan. This project will be hosted at the NETL Pittsburgh, PA campus. Although material modeling with artificial Intelligence/machine learning (AI/ML) applications and experimental instrumental