The Radiation Epidemiology (REB) and Biostatistics Branches (BB) of the Division of Cancer Epidemiology and Genetics at the National Cancer Institute/National Institutes of Health/Department of Health and Human Services seek qualified applicants for post-doctoral fellowships for interdisciplinary research on statistical issues in studying the role of radiation in cancer etiology. The Division of Cancer Epidemiology and Genetics (DCEG) of the National Cancer Institute conducts a national and international program of population- and family-based studies to elucidate the environmental and genetic determinants of cancer. REB conducts research to identify, understand, and quantify the risks of cancer in populations exposed to medical occupational, or environmental radiations, and to advance understanding of radiation carcinogenesis. BB statisticians collaborate across wide variety of studies in DCEG and develop cutting-edge statistical methods in diverse areas such as machine learning, measurement error, study design, the analysis of correlated data, statistical genetics, genomics, and risk–prediction.
The successful candidate for this position will be jointly mentored by investigators from REB and BB. Depending on one’s interest, projects may include the collaboration on radiation epidemiologic studies that require assessment of doses in studies as a prelude to risk evaluation and non-standard modeling of the dose-response relationship between radiation and cancer risk. There is particular interest in application of machine learning methods to these areas. The candidate will also be given flexibility to pursue research in broader statistical areas, such as machine learning methods and measurement error that have applications beyond radiation epidemiologic studies.
The fellow will have opportunity to work together with a highly talented team of researchers with experience in radiation, statistics, epidemiology, dosimetry, and genomics. Access to data from a large variety of one-of-a- kind cohort studies (e.g., REB’s signature cohort studies of cancer risks in children who underwent CT scans and a case-control study of children exposed to background radiation in the UK) would provide an excellent opportunity for identifying important problems, testing and validating the statistical methods and eventually applying them for improved analysis and interpretation of substantive studies. The fellow will have access to high performance computing facilities.
A PhD in Statistics, Biostatistics or in a similar quantitative field is required. Individuals with both theoretical and applied backgrounds with strong computational skills are encouraged to apply. The appointments can continue up to three years and can be extended for an additional 1-2 years. For further information about the research program of the Division of Cancer Epidemiology and Genetics at NCI, and the Radiation and Biostatistics Branches within DCEG consult the website: www.dceg.cancer.gov.
The interested candidate should send a cover letter, curriculum vitae and three letters of references to Ms Bessie Sinclair (email@example.com).
Applicants should mention in their cover letter that they are applying for the joint REB-BB postdoctoral fellowship position.
DHHS and NIH are Equal Opportunity Employers.
Telecommuting is allowed.
About National Institutes of Health-National Cancer Institute
The Radiation Epidemiology Branch (REB) in the Division of Cancer Epidemiology and Genetics (DCEG), National Cancer Institute (NCI), National Institutes of Health (NIH), Department of Health and Human Services (DHHS), is recruiting for a tenure-track/tenure statistician. The REB, which includes epidemiologists, statisticians, and dose-reconstruction experts, focuses on clarifying the roles of ionizing and non-ionizing radiation as cancer risk factors, with implications for public health and mechanisms of cancer etiology. Because radiation dose to organs and tissues can often be measured and estimated with great precision, the epidemiology of radiation carcinogenesis is highly quantified, with great scope for statisticians to make both subject-oriented and methodological contributions.
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