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Single Cell Spatial Transcriptomics Jobs in Massachusetts

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Patient-Centered Prediction Lead

Boston, MA · On-site

... bulk and single-cell, spatial transcriptomics, methylation, imaging) * Credible as a thought partner with senior R&D leaders on patient-centered prediction topics; able to engage across both ...

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Single Cell Spatial Transcriptomics information

What are the key skills and qualifications needed to thrive as a Single Cell Spatial Transcriptomics Scientist, and why are they important?

To thrive as a Single Cell Spatial Transcriptomics Scientist, you need a strong background in molecular biology, genomics, and bioinformatics, typically supported by an advanced degree (PhD or MSc) in a relevant field. Familiarity with high-throughput sequencing platforms, spatial transcriptomics technologies (like 10x Genomics Visium or NanoString GeoMx), and data analysis tools such as R or Python is essential. Critical thinking, problem-solving, and effective communication are crucial soft skills for interpreting complex data and collaborating in multidisciplinary teams. These skills and qualities are vital for generating reliable insights into cellular function and spatial organization, which drive innovative research and discovery.

What are some typical challenges faced by professionals working in Single Cell Spatial Transcriptomics, and how can they be addressed?

Professionals in Single Cell Spatial Transcriptomics often encounter challenges related to handling large, complex data sets and integrating spatial information with single-cell transcriptomic profiles. These tasks demand strong computational skills and close collaboration with bioinformaticians and other researchers. Effective communication within interdisciplinary teams is essential to ensure experimental design aligns with downstream analysis needs. Staying updated with rapidly evolving technologies and best practices also helps professionals overcome technical hurdles and produce reliable, high-impact results.

What is single cell spatial transcriptomics?

Single cell spatial transcriptomics is a cutting-edge technique that allows researchers to analyze gene expression in individual cells while preserving their spatial location within a tissue. This method combines the high-resolution insights of single-cell RNA sequencing with spatial information, enabling scientists to understand how cells interact and organize within their native environments. It is widely used in biomedical research to study tissue architecture, disease mechanisms, and cellular heterogeneity.
What are popular job titles related to Single Cell Spatial Transcriptomics jobs in Massachusetts? For Single Cell Spatial Transcriptomics jobs in Massachusetts, the most frequently searched job titles are:
What job categories do people searching Single Cell Spatial Transcriptomics jobs in Massachusetts look for? The top searched job categories for Single Cell Spatial Transcriptomics jobs in Massachusetts are:
What cities in Massachusetts are hiring for Single Cell Spatial Transcriptomics jobs? Cities in Massachusetts with the most Single Cell Spatial Transcriptomics job openings:
Single Cell Spatial Transcriptomics jobs near you
Infographic showing various Single Cell Spatial Transcriptomics job openings in Massachusetts as of June 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution.
Post Doc - Open Rank

Post Doc - Open Rank

University of Massachusetts Medical School

Worcester, MA • On-site

$48K - $66K/yr

Full-time

Posted 22 days ago

Job description

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.


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.


  •  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

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.

(manuel.garber@umassmed.edu)

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