SECT Program Application

Applicants for the Systems Epidemiology of Cancer Training (SECT) Program must have earned (or be within three months of completing) a Ph.D. in epidemiology or bioinformatics or D.V.M. or M.D. degree with relevant training from an accredited program. Fellows may be citizens or non-citizen U.S. nationals or international citizens with student visas. Fellowships are funded for three years, renewed annually pending research progress.

Postdoctoral stipends follow NIH guidelines. Fringe benefits include health insurance, paid institutional holidays, paid vacation and sick leave. In addition, support is provided for travel and registration fees for each post-doctoral fellow to attend at least one workshop and one research meeting a year. Each fellow will be allocated a computer and $10,000 per year to spend on lab supplies, relevant analytic software, research support, etc.

There are four interacting program themes to which applicants may apply. Applicants must select one or two themes of interest (view below) and contact the PI of that theme/s for further discussion.

Theme 1 -Big Data Epidemiology (led by Dr. Aaron Thrift) will focus on training in methods to manipulate, perform data linkages and analyze large-scale, multi-element patient data sets and to develop new approaches complementing existing methodologies in descriptive epidemiology (disease burden, temporal and geographic trends) and prevention, risk and outcomes research. Examples of data mining sources include the national VA administrative database, SEER-Medicare, National Cancer Database and National Readmissions Database for clinical effectiveness analyses. There are also numerous databases for geospatial analyses.

Theme 2 - Molecular and Omics Epidemiology (led by Dr. Philip Lupo), will focus on methods to integrate large-scale molecular and -omics data in population-based studies of cancer risk and outcome. These areas include genomics (germline and tumor), epigenomics, proteomics, and metabolomics. There are several CPRIT-funded Cores (described below) that trainees can leverage for the opportunity to integrate the metagenome and the microbiome in cancer epidemiologic research. Further, trainees will be able to leverage next-generation sequencing tools and large-scale databases, such as The Cancer Genome Atlas and the Encyclopedia of DNA Elements.

Theme 3 - Environmental Epidemiology (led by Dr. Elaine Symanski), teaches trainees to deploy environmental data as a foundation for sophisticated epidemiological studies. This theme is built on a socio-ecological framework that incorporates individual-level chemical and non-chemical (physical; biological; psychosocial) stressors throughout the life course, as well as place-based stressors (e.g.neighborhood deprivation) that likely play a role in explaining cancer incidence, survival and disparities. Further, in recognizing that environmental exposures can change gene expression through epigenetic mechanisms, the theme works at the intersection of genetics, environmental health, and data sciences.

Theme 4 - Computational Epidemiology (led by Dr. Chao Cheng) is a multifaceted interdisciplinary approach to provide students with a strong foundation in big data management and analysis; algorithmic, computational, and statistical thinking; and an understanding of computer systems. We will provide training in the algorithms needed for evaluating both big and sparse data that arise from ‘omics types of applications, imaging or pathology-derived and medical records. There will be training in machine learning, data curation and transformation and integration; applying model-driven methods with data mining approaches as well as new methods and data sources for extending synthetic populations. 

Theme 5 - Social and Behavioral Epidemiology (Behavioral and social science researchers are essential for advancing research on reducing health disparities. Newly available data and statistical approaches can deepen our understanding of  fundamental drivers of health and increase our capacity to improve population health and reduce disparities. The SECT Social and Behavioral Epidemiology theme provides interdisciplinary training that connects traditional behavioral and social science expertise with the rapidly evolving technical repertoire of computational epidemiology and bioinformatics approaches. Fellows in this theme will apply advanced analytic tools to develop novel datasets that integrate multiple data streams including omics, clinical (e.g., medical records), digital (e.g., electronic daily diary data, wearables), and population-level data to conduct research on behavioral and social processes underlying health disparities.

To apply, submit the following documents to EpiTraining@bcm.edu, addressed to the PI of the theme/s selected and copy to Dr. Aaron Thrift at aaron.thrift@bcm.edu. Attachments should be in .pdf or .doc format. Applications will be reviewed on a rolling basis by the co-directors and Internal Advisory Committee for feasibility (availability of required data, specimens); for approval by your suggested mentors; and for scientific relevance. A decision on all eligible applications will be made within one month of submission.

• Transcripts of your graduate career and master's level education in epidemiology
• Academic CV
• One-page letter of intent

After discussion with the theme leader/s, you can prepare a letter of intent that should address the following:

• Specify which theme/s is/are of interest to you
• Identify at least two faculty members from our mentor list (one must be an epidemiologist) with whom you would potentially like to work.
• A proposed topic for your research.

For applicants who are considered to meet the criteria, and the research is deemed feasible, a phone interview may be scheduled. If your application is under consideration, you'll be asked to provide the following documents:

• A two-page description of your career goals and an outline of the proposed research.
• Signed approval from your prospective mentors (at least one mentor must be an epidemiologist).
• Three letters of recommendation.

The most competitive candidates will be invited for an interview at Baylor College of Medicine, where they will present their research and meet with prospective program mentors. Airfare and accommodations will be provided.