Postdoctoral Research Fellow - Multiomics and Causal Inference

About the role

The Division of Cardiovascular Medicine at the University of Michigan is expanding a nationally and internationally funded research program at the cutting edge of cardiovascular medicine. Led by Drs. Venkatesh Murthy and Sascha Goonewardena, the program fuses artificial intelligence, advanced cardiac imaging, multiomics (proteomics, metabolomics, and genomics), and cardiometabolic disease biology to develop precision diagnostics and identify novel therapeutic approaches, with a particular focus on coronary microvascular disease and other cardiovascular conditions that disproportionately affect women and remain poorly served by existing diagnostic tools.

This is a rare opportunity to join a high-impact, well-funded program doing science that matters. This postdoctoral position offers an exceptional opportunity for a highly motivated researcher to lead the hypothesis-driven discovery work of the program's omics and causal inference work streams, under the primary supervision of Dr. Murthy, with close collaboration with Dr. Goonewardena and a broad network of national and international research partners.

Responsibilities

• Develop and apply analytical pipelines integrating high-throughput proteomic data with metabolomic, genomic, and clinical datasets from large prospective cohort studies to identify biological signatures of coronary microvascular disease and cardiometabolic disease
• Perform causal and genetic inference analyses - including Mendelian randomization and mediation analysis - in biobank-scale datasets
• Prepare first-author manuscripts for peer-reviewed journals and present work at national and international scientific meetings
• Contribute to grant applications, progress reports, and preliminary data generation for future funding
• Participate in laboratory meetings, journal clubs, and collaborative program activities
• Contribute to mentoring of junior trainees

Required Qualifications

• Doctoral degree (PhD, MD/PhD, or MD) in computational biology, bioinformatics, biostatistics, cardiovascular biology, epidemiology, or a closely related field
• Experience with proteomic, metabolomic, or other omic data analysis in a research context
• Proficiency in R and/or Python for statistical analysis and pipeline development
• Familiarity with causal inference or genetic epidemiology methods (e.g., Mendelian randomization, mediation analysis)
• Strong scientific writing and oral communication skills
• Demonstrated ability to drive projects to completion independently

Desired Qualifications

• Experience with large prospective cohort or biobank datasets (CARDIA, MESA, Framingham Heart Study, UK Biobank, or similar)
• Experience with high-throughput proteomic platforms (Olink or SomaScan)
• Background in cardiovascular biology, vascular biology, or cardiometabolic disease
• Track record of peer-reviewed publications relative to career stage
• Familiarity with multi-omic data integration
• Familiarity with HPC or cloud computing environments