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HomeCompaniesCareers Umms Icims ComPost Doc - Open Rank

Post Doc - Open Rank

Careers Umms Icims Com · Worcester, MA, US · Active · iCIMS

Job facts

FieldValue
CompanyCareers Umms Icims Com
TitlePost Doc - Open Rank
Normalized title-
Department / team-
LocationWorcester, MA, United States
Work model-
Employment typeOTHER
Salary-
Statusactive
ATS provideriCIMS
Posted / first seen2026-05-22 / 2026-05-31
Changed / last seen2026-06-02 / 2026-06-06

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Linked records

CompanyCareers Umms Icims Com
Source3ff34fa3-00ac-443f-947d-2d4ae9d2d3ba
ATS provideriCIMS

Description

Overview 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. Responsibilities 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. Qualifications 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 Additional Information 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 Genomics and Computational Biology. ([email protected]) #LI-KR1

Full job record

Job ID1d25d94a7a294ee14ca1aa8410339c83a7734f26
Org ID042906d2-b115-4d13-ba24-6323b4e016d0
Source ID3ff34fa3-00ac-443f-947d-2d4ae9d2d3ba
Board ID3ff34fa3-00ac-443f-947d-2d4ae9d2d3ba
Providericims
Provider Job Key48886
TitlePost Doc - Open Rank
Normalized Title
Statusactive
Activeyes
Location TextWorcester, MA, US
Department
Team
Employment TypeOTHER
Workplace Type
Remote Policy
CountryUnited States
RegionMA
CityWorcester
Salary RawOverview 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. Responsibilities 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. Qualifications 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 Additional Information 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 Genomics and Computational Biology. ([email protected]) #LI-KR1
Salary Min
Salary Max
Salary Currency
Salary Period
Source URLhttps://careers-umms.icims.com/jobs/48886/post-doc---open-rank/job
Apply URLhttps://careers-umms.icims.com/jobs/48886/post-doc---open-rank/job
First Seen At2026-05-31 18:39:54Z
Last Seen At2026-06-06 20:01:14Z
Last Checked At2026-06-06 20:01:14Z
Last Changed At2026-06-02 13:06:35Z
Inactive At
Source Posted At2026-05-22 04:00:00Z
Source Updated At2026-05-13 11:47:48Z
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Parsed Structured
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