Sort by
Refine Your Search
-
principles, research and evaluation methods, and professional development. You will also receive ongoing feedback on technical writing, presentations, and research activities. By the end of the fellowship, you
-
or technical projects. Activities may include, but are not limited to, Interpreting data and analyzing results using appropriate methods, techniques, and visualizations. Practice identifying, analyzing
-
partnership research, and implementation science methods. Gain experience collecting, organizing, analyzing, and interpreting quantitative data related to public health partnerships and systems performance
-
, and environmental factors associated with diabetes incidence, prevalence, complications, and trends. Learn and apply advanced statistical and machine learning methods, including cluster analysis and
-
. Learning Objectives: Under the guidance of a mentor, you will be able to learn to: apply artificial intelligence, statistical modeling, and agricultural data science, develop methods in predictive analytics
-
Respiratory Viruses Division (CORVD), National Center for Immunization and Respiratory Diseases (NCIRD), CDC. You will receive training in advanced molecular methods, next-generation sequencing (NGS), viral
-
aerial vehicle (UAV) imagery collection and processing, deep learning methods, and rangeland vegetation communities in Oregon and Idaho as part of an interdisciplinary team including researchers in plant
-
will support genome-wide prediction of variant effects across pathogen populations represented in USDA-ARS culture collections. Simultaneously, protein language models and structural methods will
-
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
-
, and environmental factors associated with diabetes incidence, prevalence, complications, and trends. Learn and apply advanced statistical and machine learning methods, including cluster analysis and