49 machine-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:" Fellowship positions at Zintellect
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scientific or statistical techniques. • Present experimental findings to the research community at conferences or other military/scientific venues. • Document experimental procedures and results. • Develop and
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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 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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, BioPermit. Training as a member of the CDC IPP, you will collaborate with computer and health scientists to develop enhancements for BioPermit and improvements to the electronic database. Additionally, you
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& Amputation Center of Excellence (EACE) is a unique organization within the Department of War (DoW) consisting of teams of researchers embedded at the point of care within multiple Military Treatment Facilities
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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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coursework and/or a background in transportation, collection analysis, data analysis, computer programming, technology, outreach, and/or communications. Have a demonstrated ability to meet established
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. Preferred skills: Depending on the research project objectives, the selected candidate may need to show proof of a valid U.S. State Driver’s License and provide proof of an active U.S. auto insurance policy
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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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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