The Laboratory of Epidemiology and Population Sciences (NIA) and the Chronic Disease Epidemiology Group (NIEHS) in the NIH Intramural Research Program are seeking a talented and motivated post-doctoral fellow to participate in research to develop a multi-dimensional index of aging-related exposures using existing epidemiological studies at the NIH and other large administrative databases. This joint Fellowship is aimed to train cross-discipline researchers in the fields of Environmental and Aging Epidemiology, with an emphasis on data science-based methodologies that can be applied to understand the environmental impact on aging. The Fellow’s work is expected to be transformative in overcoming barriers to data interoperability and integration, ultimately leading to breakthroughs in research on the underlying environmental, lifestyle, socioeconomic, climate, and aging-related drivers of health.
Together with a multi-disciplinary team of mentors, the Fellow will 1) identify, access, and abstract data elements for a multi-factor index that includes a specific set of environmental, (sub)-clinical mental and physical disease, and sociobehavioral exposures relevant for predicting aging-related outcomes and 2) investigate and apply advanced data science methods, including machine learning and spatio-temporal analyses, to develop an index that integrates diverse geospatial data sets with individual-level longitudinal health data.
Preference will be given to candidates with demonstrated experience in one or more of the following areas:
Management and analysis of data relevant to environmental health (e.g., census, air pollution, roadway, spatiotemporal data) and aging epidemiology (e.g., physical and cognitive function)
Experience converting heterogeneous data to common reference frames and terminologies
Experience conducting analyses of large spatio-temporal datasets
Advanced statistical programming experience (SAS, R, Linux and/or Python)
Applicants must have a Ph.D. or equivalent degree in epidemiology, statistical genetics, biostatistics, computational biology, or another related field. Successful applicants are expected to demonstrate a high level of independence, and abilities to learn new approaches and techniques; to think creatively about the research; and to work within multidisciplinary group.
Applicants should submit the following materials to Tamara.Burrowes@nih.gov
Statement describing areas of research interest
Curriculum vitae with bibliography.
Copies of one to two recent publications.
Contact information for three persons serving as references.
The NIH is dedicated to building a diverse community in its training and employment programs and encourages the application and nomination of qualified women, minorities, and individuals with disabilities.