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computational methods for single-cell and genomic data analysis, in collaboration with the lab’s methods-development efforts. • Develop and apply flow cytometry, molecular biology, and cell biology assays
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Center for Drug Evaluation and Research (CDER) | Silver Spring, Maryland | United States | about 5 hours ago
estimating treatment effects in the presence of intercurrent events whose occurrence may itself carry prognostic information. Methods of interest could include survivor average causal effect (SACE) estimators
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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
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Center for Drug Evaluation and Research (CDER) | Silver Spring, Maryland | United States | about 5 hours ago
just medicines. Research Project: Under the guidance of a mentor, you will implement two-stage meta-analytic models and weighted least squares correlation methods to estimate the surrogacy strength
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: Strong experience with flow cytometry, murine experiments, ELISA, and bacterial culture methods Some experience with microbiome methods and an interest in spatial transcriptomics Demonstrated skills in
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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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affiliated with Columbia University. Duties and Responsibilities: Construct air pollution and meteorological data prediction models using machine-learning based methods Conduct epidemiologic analyses studying
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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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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