Postdoctoral Associate - Pharmacoepidemiology/Health Informatics

Postdoctoral Associate - Pharmacoepidemiology/Health Informatics

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Position Details

Position Information

Recruitment/Posting Title Postdoctoral Associate - Pharmacoepidemiology/Health Informatics

Department Institute for Health, Health C

Salary Details A minimum of $67,000 is budgeted for this position.

Offer Information

The final salary offer may be determined by several factors, including, but not limited to, the candidate’s qualifications, experience, and expertise, and availability of department or grant funds to support the position. We also take into consideration market benchmarks, if and when appropriate, and internal equity to ensure fair compensation relative to the university’s broader compensation structure. We are committed to offering competitive and flexible compensation packages to attract and retain top talent.

Benefits

Rutgers provides a comprehensive benefits package to eligible employees. The specific benefits vary based on the position and may include:

• Medical, prescription drug, and dental coverage

• Paid vacation, holidays, and various leave programs

• Competitive retirement benefits, including defined contribution plans and voluntary tax-deferred savings options

• Employee and dependent educational benefits (when applicable)

• Life insurance coverage

• Employee discount programs

• Posting Summary

• Postdoctoral Research Associate- The Postdoctoral Research Associate will join a multidisciplinary research team focused on cognitive aging and health and Alzheimer’s disease and related dementias. Reporting to the Principal Investigator on grant-funded projects, the individual will play a key role in supporting laboratory operations and advancing ongoing research initiatives.

Primary responsibilities include:

• Designing and executing observational cohort studies examining drug-drug interactions and dementia risk using large-scale EHR and administrative claims data.
• Leading statistical analysis (methods may include IPTW-weighted Cox regression, active comparator new-user designs, self-controlled case series, propensity scoring).
• Developing