Sort by
Refine Your Search
-
, 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
-
Objectives: Build competency in designing and planning science studies, including performance measurement and mixed-methods data collection. Strengthen skills in applying quantitative and qualitative analysis
-
, gaining exposure to public health research, evaluation, and scientific operations that support agency priorities. The participant will strengthen skills in epidemiologic methods, scientific literature
-
Microsoft Office applications and familiarity with statistical analysis methods and software. Critical thinking and scientific judgment to evaluate complex environmental health data and site conditions to
-
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
-
wildlife. Use molecular techniques, including CRISPR-based methods and cloning, for vaccine and diagnostic development. Describe microbiological, pathological, and immunological approaches used to study
-
of several scientists, you will gain exposure to field and laboratory methods used to understand soil-plant-water interactions, crop needs, and conservation processes. Specific learning activities include
-
., 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
-
machine learning methods, including cluster analysis and predictive modeling, to identify distinct phenotypes of diabetes and characterize factors associated with disease onset, progression, complications
-
learning methods for process automation within SharePoint and Microsoft enterprise platforms. Under the guidance of a mentor, the participant will explore research approaches related to large language model