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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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of a scientific mentor, you will gain experience and learn to conduct rigorous data analysis and cross-project data synthesis, identify critical technical and data gaps, and determine future applied
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interacts with the human body—making it a key factor in both product performance and consumer satisfaction. You will also apply statistical and machine-learning tools to explore how cotton fiber properties
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quality and consumer satisfaction. You will also apply statistical and machine-learning tools to explore how physical and chemical fiber parameters relate to dye uptake behavior, dyebath exhaustion, color
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, and environmental factors associated with diabetes incidence, prevalence, complications, and trends. Learn and apply advanced statistical and machine learning methods, including cluster analysis and
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to complement your education and support your academic and professional goals. Along the way, you will engage in learning activities and research in several areas. These include, but are not limited to: Assess
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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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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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reports/visualizations and presentations, and collaborating with multidisciplinary teams. Learning Objectives: You will train with CDC’s Center for Forecasting and Outbreak Analytics, gaining hands
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environmental factors associated with CKD incidence and trends. Apply advanced statistical and machine learning methods, including semi-supervised cluster analysis, to characterize populations with diabetes and