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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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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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will learn how to: • Conduct in vivo preclinical research experiments focusing on neurosensory studies. • Conduct in vitro and ex vivo combat casualty care studies. • Compile and analyze data using
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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
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Experience or interest in artificial intelligence (AI), machine learning, or AI-assisted workflow automation Experience with software testing, beta testing, user acceptance testing, or information system
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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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anticipated to receive by July 2027. Preferred skills: Academic training in computer science, artificial intelligence or machine learning, data science, bioinformatics, computational biology, epidemiology