The Biostatistics Branch at the National Cancer Institute, National Institutes of Health located in Bethesda, Maryland, USA, is looking for a postdoctoral fellow interested in developing statistical methods for analyzing novel types of data from studies of cancer. Such data include high dimensional genetics and biomarker observations, data from electronic medical records, environmental and spatial data, and longitudinal and survival data. Another area of interest is prediction modeling and assessment of model performance to help translate findings into public health applications. The postdoctoral fellow will lead projects to develop novel statistical and computational methods and have opportunities to collaborate within an inter-disciplinary environment to build their methodological and collaborative research program. The candidate is expected to participate in research team efforts, and lead several projects, leading to publications. Access to high performance computing facilities and data from large, influential studies using state-of-the-art technologies facilitate publishing statistical methodology that can have a high impact on current practice. We anticipate having multiple positions.
Candidates should have (or expect to have soon) a PhD in statistics, biostatistics, mathematics or a related field and solid statistical training and strong computational skills. US citizenship is not required. Preference will be given to candidates interested in applied problems, and with superior methodological and communication skills. The stipend is commensurate with training and relevant research experience. Applicants should send: 1) a curriculum vitae; 2) a statement of research interests and 3) three letters of reference to Ruth Pfeiffer Pfeiffer@mail.nih.gov, Biostatistics Branch, National Cancer Institute. For further information on the Biostatistics Branch see https://dceg.cancer.gov/about/organization/programs-ebp/bb.
HHS and NIH are Equal Opportunity Employers
About National Cancer Institute
BB statisticians develop statistical research programs and actively collaborate both in cutting-edge studies of genetic, lifestyle, and other environmental causes of cancer, as well as in studies of cancer prevention, descriptive and clinical epidemiology. Statistical research is typically motivated by challenges encountered in DCEG studies, such as choosing an efficient study and sampling design, optimally combining data from multiple sources such as electronic medical records, genetic data bases, disease and bio-specimen registries, as well as designing validation studies and methods to evaluate and correct for measurement error in exposures and disease outcomes. The branch has active methodological research programs in areas that include 1) absolute risk prediction, 2) analysis of longitudinal and survival data, 3) analysis and temporal and spatially related incidence data, and 4) the analysis of “omics” data that includes the analysis of data from cutting-edge next generation sequencing.