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Harvard University Postdoctoral Research Fellow in Causal Inference for Cancer Control in Cambridge, Massachusetts


Title Postdoctoral Research Fellow in Causal Inference for Cancer Control

School Harvard T.H. Chan School of Public Health

Department/Area Epidemiology

Position Description

The Department of Epidemiology at the Harvard T.H. Chan School of Public Health studies the frequency, distribution, and determinants of disease in humans, a fundamental science of public health. In addition to pursuing ground-breaking global research initiatives, we educate and prepare future medical leaders and practitioners as part of our mission to ignite positive changes in the quality of health across the world.

Dr. Barbra Dickerman at the Harvard T.H. Chan School of Public Health invites motivated researchers to apply for a postdoctoral research fellow position in causal inference for cancer control.

The successful candidate will apply modern causal inference methods to large health databases (e.g., electronic health records) to inform decision-making about cancer prevention, early detection, and treatment. Multiple projects are available, including the (1) evaluation of drugs with identified repurposing potential for cancer prevention, (2) evaluation of dynamic (adaptive) screening strategies for cancer, and (3) investigation of the optimal timing and sequencing of cancer treatments.

Rich opportunities for professional development are also available, including support for attending conferences, delivering talks, teaching, and mentoring, among other activities.

The postdoctoral fellow will be supervised by Dr. Barbra Dickerman and work closely with other team members, including faculty and researchers in the CAUSALab and Zhu Family Center for Global Cancer Prevention, as well as external collaborators. Our team conducts cutting-edge research using large health databases to improve health decision-making. Results from our research have informed recommendations for the treatment and prevention of multiple diseases and contributed to the innovation of methods to generate valid evidence from real world data.

PLEASE NOTE : The finalist will be required to complete both the Harvard University and U.S. Veterans Administration background screening processes.

Basic Qualifications

Education Requirements

  • A doctoral degree in epidemiology, biostatistics, computer science, or a related quantitative field

Experience Requirements

  • Research experience in causal inference

Technical Requirements

  • Strong programming skills in SAS or R

    Additional Qualifications

Preferred Experience and Skill Requirements

  • Experience in the analysis of large health databases (e.g., EHR data)

  • Excellent written and verbal communication skills

  • Highly organized with strong attention to detail and accuracy

  • An ability to work collaboratively and independently

    Additional Information: Per university guidelines, postdoc appointments are considered to be on-campus, full-time positions. Per university payroll tax guidelines all applicants must reside in an acceptable payroll states or be willing to relocate to: Massachusetts, New Hampshire, Rhode Island, Connecticut, Maryland, Vermont or New York.

    PLEASE NOTE : Due to funding requirements, we are only able to consider U.S. Citizens or permanent residents at this time.

    Special Instructions

    Contact Information

For additional questions about the position, please contact Dr. Barbra Dickerman

Contact Email

Equal Opportunity Employer

We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, gender identity, sexual orientation, pregnancy and pregnancy-related conditions or any other characteristic protected by law. The Harvard T.H. Chan School of Public Health is committed to upholding the values of diversity, equity, and inclusion in our hiring processes. Women and individuals from underrepresented racial and ethnic minority groups are strongly encouraged to apply.

Minimum Number of References Required 2

Maximum Number of References Allowed 2


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