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gaps in dose-selection methodologies to support regulatory decision-making and future pediatric drug development strategies. Learning Objectives: You will meet regularly with your FDA mentor and a
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, 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 (ML), to help solve complex agricultural
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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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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 dosing strategies — in ways that complement or improve upon conventional development paradigms. Learning Objectives: As a participant, you will have structured learning opportunities in regulatory science
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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