Post Doctoral Associate (Multi-omics Life Course Research

Post Doctoral Associate (Multi-omics Life Course Research)

The Department of Biostatistics and Epidemiology at the Rutgers School of Public Health located in Piscataway, NJ is seeking a Post Doctoral Associate to join an interdisciplinary research program focused on pregnancy, life-course cardiometabolic health, and the developmental origins of health and disease.

The Post Doctoral Associate will work with longitudinal pregnancy and birth cohort data to investigate how metabolic and molecular characteristics during pregnancy contribute to maternal and offspring health across the life course. Current projects integrate epidemiologic data with metabolomics, proteomics, and other high-dimensional molecular measures to study pregnancy cardiovascular health, adverse pregnancy outcomes, placental biology, and later cardiometabolic disease.

The associate will have opportunities to lead manuscripts, develop independent research questions, contribute to NIH-funded and collaborative studies, present findings at scientific meetings, and participate in grant development. The position will provide training and mentorship in life-course epidemiology, pregnancy and perinatal epidemiology, and the analysis of high-dimensional molecular data.

This is a 12-month full-funded position, with additional years dependent on funding and satisfactory performance.

Duties include:

• Lead analyses of longitudinal epidemiologic and molecular data.
• Develop and apply statistical approaches for metabolomics, proteomics, and related high-dimensional data.
• Prepare first-author manuscripts and scientific presentations.
• Collaborate with epidemiologists, biostatisticians, clinicians, and laboratory scientists.

Qualifications

Minimum Education and Experience

Certifications/Licenses

Required Knowledge, Skills, and Abilities
- Doctoral degree in epidemiology, biostatistics, public health, population health, or a related quantitative field by the position start date. Applicants in All But Degree Status will be considered for interviews. Strong quantitative and epidemiologic training. Experience conducting statistical analyses in R or a comparable programming environment. Experience working with complex or high-dimensional health data. Demonstrated ability to communicate scientific findings through manuscripts, presentations, or other scholarly products.

Interest in life-course epidemiology, the developmental origins of health and disease, pregnancy and perinatal health, or cardiometabolic health. Ability to work both independently and collaboratively within an interdisciplinary research team.

Preferred Qualifications
- Experience analyzing metabolomics, proteomics, lipidomics, transcriptomics, or other high-dimensional molecular data. Familiarity with methods such as dimension reduction, clustering, penalized regression, multiple-testing correction, pathway analysis, network analysis, or multiomics integration. Experience with longitudinal cohort studies, pregnancy or birth cohorts, electronic health record data, or repeated-measures analyses. A record of peer-reviewed publication and interest in developing an independent research program. Candidates do not need to have experience in every preferred area.

Applicants with strong epidemiologic and quantitative training who are interested in expanding their expertise in omics and life-course research are encouraged to apply.