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| Title | Postdoctoral Research Position in Biostatistics |
|---|---|
| School | Harvard T.H. Chan School of Public Health |
| Department/Area | Biostatistics |
| Position Description | The Harvard TH Chan School of Public Health’s Department of Biostatistics is seeking a highly motivated postdoctoral research fellow to pioneer cutting-edge statistical and machine-learning approaches for the analysis and synthesis of diverse and complex biomedical datasets. This position presents an exceptional opportunity to contribute to the advancement of methodologies that produce reliable real-world evidence from vast biomedical repositories, including but not limited to electronic health records (EHRs), biobanks, medical claims, and health surveys. Key areas of research interest include but not limited to: (1) methods to synthesize real-world evidence from multiple sources, addressing various types of data heterogeneity (2) methods for improving the generalizability and transferability of algorithms across different healthcare systems and addresses the biases caused by lack of representation of certain minority and disadvantaged populations (3) methods to make multi-institutional collaborative learning more efficient, by designing data harmonization tools and statistical methods based on summary-level statistics with privacy protection. The postdoctoral research fellow will also work with domain experts and collaborators to implement the methods in large clinical research networks including OHDSI, 4CE, PCORnet, eMERGE and psycheMERGE to study mental disorders, cancers, and cardiovascular diseases. |
| Basic Qualifications | 1. Doctoral degree in Biostatistics, Statistics, Computer Science, or a related quantitative field. 2. Strong theoretical training in statistics or strong programing skills (R, Python, or C++) are desired. 3. Excellent communication skills and good track record of writing scientific papers. |
| Additional Qualifications | Please submit:
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| Special Instructions | |
| Contact Information | Trevor Bierig |
| Contact Email | [email protected] |
| 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. |
| Minimum Number of References Required | 3 |
| Maximum Number of References Allowed | 5 |
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