Postdoctoral Research Associate – Population Genetics & Machine Learning

About the role

The Garber Lab at UMass Chan Medical School seeks a Postdoctoral Research Associate to advance research on the genetic and molecular mechanisms of autoimmune skin diseases. The role combines population genetics, statistical modeling, and cutting‑edge single‑cell and spatial multi‑omics to translate genomic insights into personalized medicine.

Key responsibilities

• Lead integration of genomic and clinical datasets, including QTL mapping (eQTL, sQTL, caQTL) across single‑cell and bulk modalities.
• Develop and apply polygenic risk scores and causal inference models to predict disease onset, progression, and treatment response.
• Implement machine‑learning and statistical‑genetics frameworks for longitudinal clinical, environmental, and wearable data.
• Design computational pipelines for spatial transcriptomics and spatial genomics to uncover cellular drivers of local inflammation.
• Contribute to novel computational methods that merge genetics with spatial and temporal immune responses.

Required profile

• Ph.D. in Genetics, Computational Biology, Bioinformatics, Biostatistics, Computer Science, or a related discipline.
• Demonstrated expertise in population genetics, statistical modeling, or machine learning.
• Experience analyzing large‑scale genomic data (GWAS, QTL, PRS, multi‑omics integration).
• Strong programming ability in R or Python; familiarity with Bayesian modeling, causal inference, or deep learning is a plus.
• Excellent communication skills and a collaborative, interdisciplinary mindset.

Required skills

• Python
• R
• Population genetics
• Statistical modeling
• Machine learning
• GWAS analysis
• QTL mapping (eQTL, sQTL, caQTL)
• Polygenic risk scores
• Multi‑omics integration
• Bayesian modeling
• Causal inference
• Deep learning (optional)
• Spatial transcriptomics analysis
• Spatial genomics data handling

What we offer

• Opportunity to develop and publish innovative computational methods.
• Contribution to high‑impact translational studies of autoimmunity.
• Access to state‑of‑the‑art genomics technologies and a vibrant computational biology community.