Post Doc - Open Rank

University of Massachusetts • Worcester, MA

64d

About The Position

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. The successful candidate will lead analyses spanning genomic and clinical data integration, including: 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. 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.

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
• Excellent communication skills and enthusiasm for collaborative, interdisciplinary research

Nice To Haves

• Familiarity with Bayesian modeling, causal inference, or deep learning is a plus

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

Benefits

• paid time off
• medical
• dental
• vision coverage
• participation in a 401(a)-retirement plan
• the option to contribute to a voluntary 403(b) plan

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Industry: Educational Services
• Education Level: Ph.D. or professional degree