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trials, multiple endpoints, adaptive designs, Bayesian design and analysis methods, estimands, meta-analyses, benefit-risk analyses, subgroup analyses, biosimilars, patient experience data, bioequivalence
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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
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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
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partners. You will analyze complex datasets, develop and document analytical methods, and contribute findings to policy-relevant outputs. This will include relational databases such as SQL and PostgreSQL
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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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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
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in the one of the relevant fields. Degree should be anticipated to receive by 12/31/2026. Preferred skills: Experience in quantitative research methods, including but not limited to epidemiological
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their research. Additional funds are available for supplies and travel essential for the fellow's research. Research Project: Under the guidance of a mentor, you will apply computational methods for identifying
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genetic, genomic, and phenotypic datasets, to support research and crop improvement. Basic and applied research is also conducted within this project. You will use methods in computational biology to