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project of this subgroup focuses on the latter in the development of rapid detection and/or identification/confirmation methods for specific foodborne pathogenic bacteria (e.g., Shiga toxin-producing E
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this subgroup focuses on the development of rapid, field-deployable detection methods for specific foodborne pathogenic bacteria (e.g., Shiga toxin-producing E. coli, Salmonella, Listeria monocytogenes). Analysis
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and often different from the canonical types of data used to benchmark machine learning (ML) algorithms. In this opportunity, we will be evaluating how state-of-the-art ML techniques can be used
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apply molecular biology concepts to characterize immunoglobulin-E (IgE) binding to food allergens. Learning Objectives: Under the guidance of your mentor, you will: Develop knowledge of the sequence and
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formation, removal and sanitation of food - contact surfaces Learn to optimize processes related to emerging interventions to control pathogens such as Salmonella spp., E. coli, and L. monocytogenes, in
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. Research Project: This opportunity is within the Research and Evaluation Team (R&E) in the Office of Emergency Risk Communication (OERC), Office of Communication, at CDC. The mission of OERC’s Research and
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USDA-ARS Postdoctoral Research Opportunity: Development of Novel Vaccines for Poultry Viral Diseases
of the agency is to provide global leadership in agricultural discoveries through scientific excellence. Research Project: You will be part of the research team learning about developing recombinant Marek's
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, data engineering, data analytics, artificial intelligence, machine learning, deep learning, natural language processing, and automation using modern tools and techniques. During this fellowship, you will
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, K., Bell III, J.F., Bosak, T., Broz, A.P. and Clavé, E., 2025. Redox-driven mineral and organic associations in Jezero Crater, Mars. Nature, 645(8080), pp.332-340. Field of Science: Planetary Science
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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