Post Doc - Open Rank

University of Massachusetts Medical School • Worcester, MA

63d

About The Position

Postdoctoral Position in Population Genetics and Machine Learning of Autoimmunity The Garber Lab at the University of Massachusetts Chan Medical School (UMass Chan) invites applications for a Postdoctoral Research Associate to join our multidisciplinary team studying the genetic and molecular mechanisms driving autoimmune and inflammatory skin diseases. Our group integrates population genetics, statistical modeling, and single-cell and spatial multi-omics to understand how genetic variation and immune pathways converge to cause disease. We are a core component of the VIGOR study (vigor.umassmed.edu), a large-scale longitudinal study of vitiligo and related autoimmune conditions, and collaborate extensively with clinical and computational teams to translate genomic insights into personalized medicine approaches. Our overarching goal is to define the genetic underpinnings of autoimmune skin diseases by understanding how genetic variability alters immune cell responses that tilt the balance toward autoimmunity. Building on our recent studies that revealed disease-associated dendritic cell states and cytokine-driven spatial programs of inflammation, the postdoctoral researcher will have access to a rich resource of single-cell, spatial, and longitudinal clinical datasets generated by our NIH-funded consortium. The Garber Lab is part of a vibrant computational and systems biology community at UMass Chan, providing access to state-of-the-art genomics technologies, clinical cohorts, and cross-disciplinary mentorship. Our team values rigorous quantitative science, open collaboration, and mentorship-driven career development. Interested candidates should send a CV, a brief statement of research interests, and contact information for three references to Manuel Garber, Ph.D., Professor of Molecular, Cell, and Cancer Biology ([email protected])

Requirements

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

Responsibilities

• Performing QTL mapping (eQTL, sQTL, and caQTL) across single-cell and bulk data modalities
• Developing and applying polygenic risk scores and causal inference models to predict disease onset, progression, and treatment response
• Implementing machine learning and statistical genetics frameworks to integrate longitudinal clinical, environmental, and wearable-derived data
• Designing computational approaches for spatial transcriptomics and spatial genomics data to identify key cellular and molecular drivers of local inflammation
• Contributing to the development of computational methods for integrating genetics with spatial and temporal immune responses
• The position provides opportunities to develop and publish innovative computational methods and to contribute to high-impact translational studies of autoimmunity.

What This Job Offers

• Job Type: Full-time
• Industry: Educational Services
• Education Level: Ph.D. or professional degree
• Number of Employees: 5,001-10,000 employees