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 responsesThe 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.
