Postdoc Fellow: Life-Course Multi-Omics Epidemiology

Massachusetts General Hospital; Boston (MA); On-site; USD 70,000 - 72,000; Full time

Job summary

Massachusetts General Hospital invites applications for a post-doctoral fellow in the Center for Genomic Medicine. The role focuses on life-course epidemiology, integrated omics, women’s and children’s health, and chronic disease prevention, under Dr. Jie (Jack) Hu and Dr.

Richa Saxena. The appointment spans MGH and HMS, requires a Ph.D. in epidemiology or a related field, experience with multi-omics and population-based research, and strong programming skills in R, Python, or SAS.

Qualifications

• PhD in epidemiology, bioinformatics, biostatistics, computational biology, or MD.
• Priority for population-based research with phenotypic and/or multi-omics data.
• First (or co-first) author of peer-reviewed publications or conference presentations.
• Experience with omics platforms (genetics, genomics, epigenetics, metabolomics, proteomics).
• Strong programming skills in R, Python, and/or SAS.
• Excellent English verbal and written communication.

Responsibilities

• Characterize risk of chronic diseases among women with adverse pregnancy outcomes using large-scale EHR-based biobanks and longitudinal cohorts.
• Elucidate biological mechanisms linking adverse pregnancy outcomes to future chronic diseases via integrative multi-omics approaches.
• Identify risk factors and mechanisms of intergenerational transmission of chronic disease using multi-omics data in pregnancy studies and birth cohorts.

Skills

R programming

Python

SAS

Omics data

EHR data

Scientific communication

Education

PhD in epidemiology or related field

Job description

Massachusetts General Hospital invites applications for a post-doctoral fellow in the Center for Genomic Medicine. The role focuses on life-course epidemiology, integrated omics, women’s and children’s health, and chronic disease prevention, under Dr. Jie (Jack) Hu and Dr.

Richa Saxena. The appointment spans MGH and HMS, requires a Ph.D. in epidemiology or a related field, experience with multi-omics and population-based research, and strong programming skills in R, Python, or SAS.