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Position TitleEnvironmental Health Biostatistics Postdoctoral Fellow
The University of Rochester Medical Center (URMC) is one of the nation’s leading academic medical centers. Our T32 training grant “Training in Environmental Health Biostatistics” (T32ES007271) was first awarded in 1992 and is funded by the National Institute of Environmental Health Sciences at NIH. The training environment is achieved by a collaboration between biostatisticians and Environmental Health Sciences (EHS) researchers.
The appointee will develop and apply novel statistical methodology for projects related to Environmental Health (EH), under the mentorship of a Biostatistics faculty trainer
(Drs. Sally W. Thurston, Matthew N. McCall, Brent Johnson, Tanzy Love, Michael McDermott, David Oakes, or Robert Strawderman). The specific methodological focus may be based in part on the trainee’s interests, and will involve co-mentorship from a leading environmental health researcher.
Examples of EH topics include studies of (a) the associations between air pollution exposure and biomarkers thought to indicate increased risk of future cardiac events; (b) effects of pre- and post-natal mercury exposure from fish consumption on multiple outcomes in childhood and adolescence; (c) RNA sequencing to quantify the effect of dioxin exposure on CD8+ T-cell gene expression accounting for an unknown mixture of responders and non-responders; and (d) methods to quantify morphologic changes in microglia in response to pharmacological alterations of noradrenergic signaling in the brain.
Methodological expertise among T32 faculty trainers includes Bayesian MCMC methods, models for multiple outcomes, latent variable models, measurement error, missing data, causal inference, survival analysis, clustering, statistical genomics, molecular systems biology, and bioinformatics.
The appointee will also receive training in advanced biostatistics and in toxicology, and be involved in other collaborative work with EH researchers.
In accordance with NIEHS requirements, trainees must be a US citizen or permanent resident, and must have completed a doctoral degree in statistics or a related subject by the appointment start date.
We seek a highly motivated candidate with a strong statistical background, excellent programming skills and good communication.