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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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anticipated to receive by July 2027. Preferred skills: Academic training in computer science, artificial intelligence or machine learning, data science, bioinformatics, computational biology, epidemiology
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the application of machine learning/artificial intelligence (ML/AI) in environmental health. This project aligns with ATSDR's current strategic initiatives and will provide you with opportunities
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presentations, technical reports, posters, abstracts, and manuscripts. Activities may be tailored your scientific background, interests, and professional development goals. Learning Objectives: During
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and capability gaps. Learning Objectives: Through this opportunity, you will gain knowledge and practical experience in biosurveillance, emerging biological threats, public health preparedness, program
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activities, learning how to organize and synthesize information from diverse scientific programs. Under the guidance of a mentor you will perform a structured gap analysis to identify unmet research needs and
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Response is seeking a fellowship participant to receive hands-on training in the coordination, development, and implementation of advanced software systems. Through mentorship and experiential learning, you
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of multiple surveillance and administrative data sources. Development of reproducible analytical workflows using programming languages such as R and Python. Application of machine learning and predictive
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machine learning methods, including cluster analysis and predictive modeling, to identify distinct phenotypes of diabetes and characterize factors associated with disease onset, progression, complications
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managing diabetes using computer simulation models. Learning Objectives: You will learn: How to synthesize and translate empirical evidence on cost-effectiveness of interventions for the prevention and