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related area, including meteorology, hydrometeorology, remote sensing, surface and atmospheric modeling, or related fields. Experience in machine learning techniques are highly desirable. Please see https
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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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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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related to the IID program's operational infrastructure. Learning Objectives: Through this appointment, you will gain skills and experience with FDA's regulatory processes and the role of inactive
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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
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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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. Additional Desired Qualifications: Demonstrated success in collaborative environments. Experience with NASA remote sensing data and machine learning. Experience communicating scientific concepts to wide
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just medicines. Research Project: This educational research participation opportunity within FDA’s Office of Generic Drugs (OGD) provides a mentored learning experience focused on the evolving global
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this fellowship resides: https://www.ars.usda.gov/research/programs-projects/project/?accnNo=444836 Learning Objectives: Under guidance of a mentor, the participant will: Learn to collaborate with USDA-ARS
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emerging technologies, transportation data, policy, research and all modes of transportation across the Department. As a Fellow, you will learn to facilitate the transformation of our transportation system