Post-doctoral Research Fellow, Center for Genomic Medicine

Job Summary

Dr. Jie (Jack) Hu and Dr. Richa Saxena in the Center for Genomic Medicine (CGM) at the Massachusetts General Hospital (MGH) and Harvard Medical School (HMS), are seeking a highly motivated post-doctoral fellow candidate working in areas of life-course epidemiology, integrated omics, women's and children's health, and chronic disease prevention.

The fellow will work under direct mentorship of Dr. Jie (Jack) Hu, Instructor in the CGM and at MGH and HMS, Instructor in the Department of Epidemiology at Harvard T.H. Chan School of Public Health, and Associated Scientist at Broad Institute of MIT and Harvard. Dr. Hu's research program focuses on investigating of how genetic, molecular, lifestyle, and environmental factors contribute to disease risk across the lifespan, with an emphasizes on adverse pregnancy outcomes, cardiometabolic diseases, and neurodegenerative disorders.

Applicants interested in life-course epidemiology, chronic disease epidemiology, large-scale electronic health records (EHR)-based datasets, genetics, metabolomics, proteomics, and multi-omics integration are encouraged to apply. The new hire will be appointed as a Research Fellow at the Massachusetts General Hospital and Post-Doc at the Harvard Medical School.

Principal Duties and Responsibilities

The fellow will be situated at MGH under the guidance and mentorship of Dr. Jie (Jack) Hu. The successful candidate will have opportunities to work with clinical, genetic, and multi-omic data from large-scale EHR-based biobanks (e.g., MGB Biobank, All of Us, etc.), pregnancy / birth cohorts (e.g., Boston Birth Cohort, TOPMed BCC-PREG and nuMoM2b-HHS studies, etc.), and longitudinal cohort studies (e.g., Nurses Health Studies, Women's Health Initiative, ROSMAP, TOPMed cohorts, etc.). The fellow will lead research projects including, but not limited to:

• Characterizing the risk of various chronic diseases among women with a history of adverse pregnancy outcomes, such as gestational diabetes and preeclampsia, using large-scale EHR-based biobanks and longitudinal cohort studies.
• Elucidating the biological mechanisms linking adverse pregnancy outcomes to an increased risk of future chronic diseases later in life through integrative multi-omics approaches – including genetics, epigenetics, metabolomics, and proteomics – in large-scale population-based studies.
• Identifying risk factors and underlying mechanisms related to the intergenerational transmission of chronic disease risk, including obesity and hypertension, by leveraging phenotypic and multi-omics data in pregnancy studies and birth cohorts.

Skills & Competencies Required

The ideal candidate will have received, or expect to receive soon, a Ph.D. in epidemiology, bioinformatics, biostatistics, computational biology, or a related field, or an M.D. The degree must be completed by the fellowship start date. Priority will be given to candidates with experience in conducting population-based research using phenotypic and/or multi-omics data from EHR, cohort studies, and/or biobanks. Additional qualifications include:

• First (or co-first) author of one or more peer-reviewed scientific publications and/or presentations at scientific conferences
• Experience in conducting research using one or more omics platforms (e.g., genetics, genomics, epigenetics, metabolomics, or proteomics), or interested in such research
• Strong programming skills in R, Python, and/or SAS
• Excellent English verbal and written communication skills
• Able to work both independently and in a collaborative and interdisciplinary research environment