Postdoctoral Research Fellow in Causal Inference for Cancer Control
Job Description
Details
| 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 Chan School is committed to enriching the academic experience of our talented students and researchers by providing the critical thinking and problem-solving skills they need to address current and future challenges in public health and clinical medicine. Our faculty and research staff are global leaders in the study of the frequency, distribution, and determinants of disease in humans, known for their innovative approaches to the epidemiology of cancer, cardiovascular disease, reproductive and perinatal diseases, and other chronic diseases, as well as in epidemiologic methodology. All of this work is strengthened by deep foundations in theory and application and broad interdepartmental engagement. In addition to pursuing groundbreaking 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. The Department of Epidemiology is invested in strong mentorship within a dynamic culture of collaboration and innovation. Our unique and diverse community provides unparalleled collaborative opportunities with academic departments across Harvard and at many world-class Harvard-affiliated hospitals. The Department of Epidemiology at Harvard T.H. Chan School of Public Health invites applications for a postdoctoral research fellow position focused on the application of modern causal inference methods to complex biomedical data (e.g., electronic health records) to improve and personalize cancer control. Example projects include (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. The postdoctoral fellow will be supervised by Dr. Barbra Dickerman and work closely with collaborators at the CAUSALab and Zhu Family Center for Global Cancer Prevention. PLEASE NOTE: This position has a term appointment of 2 years from date of hire, with the possibility of extension based on continued funding availability. 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
Experience Requirements
Technical Requirements
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| Additional Qualifications | Preferred Experience and Skill Requirements
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 | barbra_dickerman@g.harvard.edu |
| 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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