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Post Doc - Open Rank
Careers Umms Icims Com · Worcester, MA, US · Active · iCIMS
Job facts
| Field | Value |
|---|---|
| Company | Careers Umms Icims Com |
| Title | Post Doc - Open Rank |
| Normalized title | - |
| Department / team | - |
| Location | Worcester, MA, United States |
| Work model | - |
| Employment type | OTHER |
| Salary | - |
| Status | active |
| ATS provider | iCIMS |
| Posted / first seen | 2026-05-22 / 2026-05-31 |
| Changed / last seen | 2026-06-02 / 2026-06-06 |
Related slices
| Page | What it contains | Open |
|---|---|---|
| Company jobs | Active postings from Careers Umms Icims Com. | Open |
| Company breakdowns | Role, location, ATS, and work model facets for this company. | Open |
| ATS provider jobs | Active postings observed through iCIMS. | Open |
| Provider filtered search | The same provider as a filtered job collection. | Open |
| City jobs | Active postings in Worcester. | Open |
| Lifecycle events | Open, update, close, and reopen events for this posting. | Open |
| Original posting | Canonical source or apply URL captured from the ATS. | Open |
Linked records
| Company | Careers Umms Icims Com |
| Source | 3ff34fa3-00ac-443f-947d-2d4ae9d2d3ba |
| ATS provider | iCIMS |
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 ID | 1d25d94a7a294ee14ca1aa8410339c83a7734f26 |
| Org ID | 042906d2-b115-4d13-ba24-6323b4e016d0 |
| Source ID | 3ff34fa3-00ac-443f-947d-2d4ae9d2d3ba |
| Board ID | 3ff34fa3-00ac-443f-947d-2d4ae9d2d3ba |
| Provider | icims |
| Provider Job Key | 48886 |
| Title | Post Doc - Open Rank |
| Normalized Title | — |
| Status | active |
| Active | yes |
| Location Text | Worcester, MA, US |
| Department | — |
| Team | — |
| Employment Type | OTHER |
| Workplace Type | — |
| Remote Policy | — |
| Country | United States |
| Region | MA |
| City | Worcester |
| Salary Raw | 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 |
| Salary Min | — |
| Salary Max | — |
| Salary Currency | — |
| Salary Period | — |
| Source URL | https://careers-umms.icims.com/jobs/48886/post-doc---open-rank/job |
| Apply URL | https://careers-umms.icims.com/jobs/48886/post-doc---open-rank/job |
| First Seen At | 2026-05-31 18:39:54Z |
| Last Seen At | 2026-06-06 20:01:14Z |
| Last Checked At | 2026-06-06 20:01:14Z |
| Last Changed At | 2026-06-02 13:06:35Z |
| Inactive At | — |
| Source Posted At | 2026-05-22 04:00:00Z |
| Source Updated At | 2026-05-13 11:47:48Z |
| Raw Payload Uri | s3://job-postings-prod-raw-590183727216/raw/provider=icims/board=careers-umms.icims.com/date=2026-06-06/2026-06-06T20-01-10-275Z-ea3b9c7c13c98998c93d65398d85311d61b443562b80de2aad3bb620eadb07c1.json |
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"description": "<h2>Overview</h2>\n<p>Postdoctoral Position in Population Genetics and Machine Learning of Autoimmunity</p>\n<p> </p>\n<p>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.</p>\n<p> </p>\n<p> </p>\n<h2>Responsibilities</h2>\n<p> </p>\n<p>The successful candidate will lead analyses spanning genomic and clinical data integration, including:</p>\n<p> </p>\n<ul>\n <li>Performing QTL mapping (eQTL, sQTL, and caQTL) across single-cell and bulk data modalities </li>\n <li>Developing and applying polygenic risk scores and causal inference models to predict disease onset, progression, and treatment response </li>\n <li>Implementing machine learning and statistical genetics frameworks to integrate longitudinal clinical, environmental, and wearable-derived data </li>\n <li>Designing computational approaches for spatial transcriptomics and spatial genomics data to identify key cellular and molecular drivers of local inflammation </li>\n <li>Contributing to the development of computational methods for integrating genetics with spatial and temporal immune responses</li>\n <li>The position provides opportunities to develop and publish innovative computational methods and to contribute to high-impact translational studies of autoimmunity.</li>\n</ul>\n<p> </p>\n<p> </p>\n<p>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.</p>\n<p> </p>\n<h2>Qualifications</h2>\n<p> </p>\n<ul>\n <li> Ph.D. (or equivalent) in Genetics, Computational Biology, Bioinformatics, Biostatistics, Computer Science, or a related field</li>\n</ul>\n<p> </p>\n<ul>\n <li>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)</li>\n</ul>\n<p> </p>\n<ul>\n <li>Strong programming skills in R or Python; familiarity with Bayesian modeling, causal inference, or deep learning is a plus</li>\n</ul>\n<p> </p>\n<ul>\n <li>Excellent communication skills and enthusiasm for collaborative, interdisciplinary research</li>\n</ul>\n<h2>Additional Information</h2>\n<p> </p>\n<p>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.</p>\n<p> </p>\n<p>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.</p>\n<p>([email protected])</p>\n<p> </p>\n<p>#LI-KR1</p>",
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