ZipRecruiter

Log In

1

Spatial Transcriptomics Jobs in Massachusetts (NOW HIRING)

UMass Med School

Post Doc - Open Rank

Worcester, MA · On-site

$48K - $66K/yr

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 ...

UMass Med School

Computational Biologist

Worcester, MA · On-site

$90K/yr

Process, analyze, and interpret large-scale datasets including bulk and single-cell RNA-seq, ATAC-seq, proteomics, and spatial transcriptomics. * Develop new analysis methods as needed and as they ...

Dana-Farber Cancer Institute, Inc.

$72K - $76K/yr

Conduct liquid biopsies and spatial transcriptomics while focusing on Kidney Cancer/GU Cancers. At Dana-Farber Cancer Institute, we work every day to create an innovative, caring, and inclusive ...

MapLight Therapeutics

Clinical Data Manager

Burlington, MA · On-site

$145K - $160K/yr

We combine cutting-edge technologies including optogenetics, in vivo physiology, and spatial transcriptomics to identify novel drug targets and develop effective therapies to address psychiatric ...

Dana-Farber Cancer Institute

$53K - $72K/yr

The candidate for this position will join a team of computational biologists to work on multi-omic sequencing datasets (DNA, RNA, epigenetic, spatial transcriptomics and spatial proteomics) in the ...

next page

Showing results 1-20

All JobsSpatial Transcriptomics JobsSpatial Transcriptomics Jobs in Massachusetts

Spatial Transcriptomics information

What is spatial transcriptomics?

Spatial transcriptomics is an advanced technique that allows scientists to measure gene expression within the spatial context of tissue samples. Unlike traditional RNA sequencing, which loses information about where each gene is expressed, spatial transcriptomics preserves the physical location of gene activity in tissues. This helps researchers better understand how cells function within their native environments and interact with neighboring cells, which is especially valuable in fields like cancer research, neuroscience, and developmental biology. The method combines microscopy, molecular biology, and computational analysis to produce detailed maps of gene expression.

What are some common challenges faced by professionals working in spatial transcriptomics, and how can they be addressed?

Professionals in spatial transcriptomics often encounter challenges related to handling large, complex datasets and integrating spatial information with gene expression data. Ensuring high-quality sample preparation and mastering advanced imaging or sequencing technologies are also frequent hurdles. These challenges can be addressed by collaborating closely with multidisciplinary teams—including bioinformaticians, molecular biologists, and imaging specialists—and staying up-to-date with the latest software tools and protocols. Continuous learning and effective communication within the team are key to overcoming technical and analytical obstacles in this rapidly evolving field.

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

To thrive as a Spatial Transcriptomics Scientist, you need a strong background in molecular biology, genomics, and bioinformatics, typically supported by an advanced degree in a life science field. Familiarity with spatial transcriptomics platforms (such as 10x Genomics Visium), next-generation sequencing (NGS) technologies, and data analysis tools like R or Python is essential. Strong problem-solving skills, attention to detail, and effective communication are important soft skills for collaborating on interdisciplinary research projects. These skills and qualities are crucial for generating high-quality spatial gene expression data and translating findings into meaningful biological insights.
What are popular job titles related to Spatial Transcriptomics jobs in Massachusetts? For Spatial Transcriptomics jobs in Massachusetts, the most frequently searched job titles are:
What job categories do people searching Spatial Transcriptomics jobs in Massachusetts look for? The top searched job categories for Spatial Transcriptomics jobs in Massachusetts are:
What cities in Massachusetts are hiring for Spatial Transcriptomics jobs? Cities in Massachusetts with the most Spatial Transcriptomics job openings:
Spatial Transcriptomics jobs near you
Infographic showing various Spatial Transcriptomics job openings in Massachusetts as of July 2026, with employment types broken down into 50% Full Time, and 50% Contract. 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 12 days ago

Job 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.
(manuel.garber@umassmed.edu)
#LI-KR1

Apply