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scientific methods and analytical approaches used to support the mission of the ATSDR Office of Innovation and Analytics (OIA). You will gain hands-on experience with computational, data science, and other
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, and maintain analytical pipelines and software; Evaluate emerging bioinformatics tools, datasets, and analytical methods; Collaborate on scientific presentations, technical documentation, posters, and
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potential public health impacts of chemical exposures. Learn ATSDR methods and frameworks for conducting environmental public health assessments and risk evaluations. Gain experience creating data
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public health insights. Train with Registry scientists to apply analytic methods using data from the National ALS Registry and Biorepository. Understand how to identify research gaps, refine research
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the network. You will also participate in the analysis and interpretation of respiratory virus hospitalization surveillance data, including applying epidemiologic and statistical methods, developing
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, clinical cohort analysis, and patient-reported outcome methods to improve understanding of ME/CFS and related infection-associated chronic conditions and illnesses (IACCIs). Research Project: A research
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., Medicare and Medicaid) to examine clinical and environmental factors associated with CKD incidence and trends. Apply advanced statistical and machine learning methods, including semi-supervised cluster
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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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physical activity promotion. Learn how to apply quantitative and qualitative analytic methods, including opportunities to use GIS, statistical software (R or Python), and emerging analytic approaches
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organizational goals. Designing and implementing a public health program evaluation using quantitative and qualitative data analysis methods. Reporting evaluation results using data visualization techniques