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Spatial Transcriptomics Jobs in Pittsburgh, PA (NOW HIRING)

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Spatial Transcriptomics information

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$47.6K

$197.5K

$388.3K

How much do spatial transcriptomics jobs pay per year?

As of Jun 4, 2026, the average yearly pay for spatial transcriptomics in Pittsburgh, PA is $197,530.00, according to ZipRecruiter salary data. Most workers in this role earn between $76,200.00 and $388,300.00 per year, depending on experience, location, and employer.

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 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 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.
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Spatial Transcriptomics jobs near you
Infographic showing various Spatial Transcriptomics job openings in Pittsburgh, PA as of May 2026, with employment types broken down into 3% Internship, 64% Full Time, 16% Part Time, 3% Temporary, 11% Contract, and 3% Nights. Highlights an 92% Physical, 1% Hybrid, and 7% Remote job distribution, with an average salary of $197,530 per year, or $95 per hour.
Post Doctoral.Post Doctoral.Associate

Post Doctoral.Post Doctoral.Associate

University of Pittsburgh

Pittsburgh, PA

$47.60K - $64.60K/yr

Other

Posted 6 days ago

Job description

Postdoctoral Associate Researcher in Computational Biology/ Bioinformatics at the Vascular Medicine Institute (VMI), the University of Pittsburgh, School of Medicine, Department of Medicine

We are seeking a highly skilled and motivated postdoctoral associate researcher to join a computational systems biology project funded by NASA, focused on understanding how spaceflight-induced stress drives systemic failure across organs and identifying therapeutic strategies to mitigate these effects. The successful Candidate will work on a high-impact, data-driven initiative aimed at uncovering how microgravity, radiation, and related stressors induce coordinated molecular and cellular dysfunction across multiple organs. The project integrates bulk, single-cell, spatial, and time-series transcriptomic datasets from both animal models and astronaut biospecimens.

Our research group operates at the intersection of computational biology, systems medicine, and translational science within the Vascular Medicine Institute (VMI). We focus on integrating large-scale omics data with mechanistic modeling to uncover systemic drivers of disease. This project provides a unique opportunity to work on NASA spaceflight data while developing broadly applicable frameworks for understanding organ dysfunction and therapeutic intervention.

Key research responsibilities

     Integrate and analyze multi-modal transcriptomic datasets (bulk RNA-seq, single-cell RNA-seq, spatial transcriptomics, and time-series data).

     Identify disease molecular signatures under spaceflight and simulated conditions.

     Construct and analyze gene regulatory networks (GRNs) to infer upstream regulators.

     Develop and apply deep learning-based drug repurposing approaches to identify compounds that reverse space flight-induced regulatory disruptions.

Required skills and qualifications:

     Ph.D. in computational biology, bioinformatics, systems biology, data science, or a related field.

     Strong experience in transcriptomic data analysis, experience with spatial transcriptomics is a plus.

     Demonstrated expertise in statistical learning or machine learning.

     Experience with network biology, pathway analysis, or gene regulatory network inference is highly desirable.

     Ability to work independently on complex, interdisciplinary projects.

Interested applicants should apply via join.pitt.edu requisition #26003096

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