Post Doctoral Associate - Lipidomics & Statistical Genomics

University of Pittsburgh • Pittsburgh, PA

2d • Hybrid

About The Position

The University of Pittsburgh School of Public Health (PI: Dr. Ryan Minster) is seeking a highly motivated Postdoctoral Associate to join an interdisciplinary research team focused on cardiometabolic health in Pacific Islander populations. This position is supported by an NIH-funded project, "Lipidomics and Structural Genomics of Cardiometabolic Health in Samoan Adults." The study integrates liquid chromatography-mass spectrometry (LC-MS)-based comprehensive lipidomic profiling, genomic variation (including structural variation), and clinical phenotypes in more than 4,000 Samoan adults from Samoa and American Samoa. The successful Candidate will lead and support analyses examining relationships among the lipidome, genetic variation, modifiable risk factors, and cardiometabolic health outcomes.
This position is intended for a motivated Candidate who, with assistance from our mentoring team, can learn about, develop, and implement lipidomics analysis workflows for the project.
Building upon our team's strong track record of mentoring Postdoctoral Associates, we will mentor the successful Candidate and provide them with additional training during their time with us.

Requirements

• PhD in Bioinformatics, Computational Biology, Biostatistics, Human Genetics, Analytical Chemistry, Metabolomics/Lipidomics, or a related field
• Strong programming skills, preferably in R
• Experience with statistical modeling and data analysis
• Ability to study, understand, and modify/extend existing lipidomics workflows by reading the literature, documentation, and computer code.

Nice To Haves

• Demonstrated hands-on experience processing lipidomics or metabolomics data from raw LC-MS outputs
• Familiarity with multi-omics or integrative analysis
• Experience contributing to peer-reviewed publications
• Ability to work collaboratively in interdisciplinary research environments

Responsibilities

• Lipidomics data processing and analysis
• Lead processing and quality control of high-dimensional lipidomics data from raw files through analysis-ready datasets
• Implement and optimize lipidomics preprocessing workflows (normalization, batch correction, feature filtering, annotation)
• Conduct statistical analyses linking lipidomic profiles with genetic and cardiometabolic phenotypes.
• Contribute to lipidome-wide association studies and integrative multi-omics analyses.
• Coordinate with lipidomics core laboratory as needed.
• Computational and statistical workflows
• Develop and maintain reproducible analysis pipelines using R or other programming languages
• Perform data quality diagnostics, including handling missing data, outlier detection, and sensitivity analyses
• Conduct regression, mixed-effects, and high-dimensional modeling approaches
• Support integration of lipidomics with genomic and structural variant datasets
• Scientific collaboration
• Assist with interpretation of lipidomics findings in a cardiometabolic and population-health context
• Contribute to manuscripts, abstracts, and grant proposals
• Present findings at lab meetings and scientific conferences
• Collaborate with an interdisciplinary team including nurse scientists, geneticists, statisticians, and epidemiologists
• Professional Development
• Seek out and take advantage of training opportunities designed to prepare you for the next steps in your career.
• Team science
• May assist with training members of our research group in R programming, statistical methods, and data management best practices
• Help establish best practices for reproducible multi-omics data processing.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: Ph.D. or professional degree