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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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will be a part of data collection, analysis, modeling and simulation for this project. Learning Objectives: Under the guidance of a mentor, you will have the opportunity to learn about drug development
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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
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Python Experience developing, testing, and refining machine learning models Experience developing HPC workflows Excellent written and oral communication skills Ability to function as a teammate in a
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from this project will contribute to ongoing collaborative learning among reviewers and will support FDA’s mission to advance model-informed drug development (MIDD) and regulatory science for pediatric
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and research in several areas. These include, but are not limited to: Adversarial location and network interdiction models Adversarial machine learning attacks and defense (e.g., against Bayesian
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baths, monitoring dye uptake, and evaluating color responses. Beginning or intermediate experience with statistical modeling or machine-learning methods, especially for exploring multivariable
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well as exploring the application of research findings to advanced 3D models such as organoids and 3D bioprinted tissues Learning about high-content, automated phenotypic drug screening pipelines against high
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to evolutionary time scales. One of the goals of the SCINet Initiative is to develop and apply new technologies, including AI and machine learning (ML), to help solve complex agricultural problems that also depend
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the soil-water-plant-air continuum using process-based models. You will learn how to take proper soil, plant and air samples that influence carbon and nitrogen dynamics and learns how soil and plant