Research Associate - Epidemiology & Causal Inference

University of Pennsylvania Perelman School of Medicine; Philadelphia (Philadelphia County); On-site; USD 85,000 - 115,000; Full time

Job summary

The University of Pennsylvania Perelman School of Medicine seeks candidates for Research Associate positions in the Academic Support Staff within the Department of Biostatistics, Epidemiology and Informatics. The appointment initially covers one year with potential renewal based on performance and funding.

Ideal candidates will have a PhD in a related field, postdoctoral experience, and a strong track record in statistical and causal inference methods for longitudinal data, with collaboration

Qualifications

• PhD or equivalent degree required; at least one year of postdoctoral experience.
• Expertise in longitudinal causal inference and quantitative methods.
• Experience with EHRs, registries, or biobank data is preferred.

Responsibilities

• Development and application of statistical, causal, and machine learning methods for analyzing complex longitudinal data structures, including electronic health records (EHRs), registry data, biobanks, pragmatic trials and cohort studies;
• Collaboration on research projects across the palliative care, critical care, and health services research spectrum, working alongside faculty in the Department and a dynamic team of clinician-scientists, epidemiologists, and biostatisticians; and
• Support for methodological development in clinical trials design, analysis, and monitoring; observational studies; and Bayesian causal inference applications.

Skills

Longitudinal causal inference

Statistical methods

Machine learning

Collaborative research

Education

PhD in Biostatistics or Epidemiology

Job description

The University of Pennsylvania Perelman School of Medicine seeks candidates for Research Associate positions in the Academic Support Staff within the Department of Biostatistics, Epidemiology and Informatics. The appointment initially covers one year with potential renewal based on performance and funding.

Ideal candidates will have a PhD in a related field, postdoctoral experience, and a strong track record in statistical and causal inference methods for longitudinal data, with collaboration