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Computational Spatial Transcriptomics Jobs in Houston, TX

MD Anderson

Postdoctoral Fellow - Immunology

Houston, TX

$46K - $63K/yr

They will integrate cancer genomics, transcriptomics, imaging, and functional studies to identify ... cell sequencing, spatial biology, or transcriptomic analyses. Experience with computational ...

MD Anderson

Postdoctoral Fellow - Immunology

Houston, TX

$46K - $63K/yr

They will integrate cancer genomics, transcriptomics, imaging, and functional studies to identify ... spatial biology, or transcriptomic analyses. • Experience with computational analysis of genomic ...

MD Anderson

Postdoctoral Fellow - Immunology

Houston, TX · On-site

$46K - $63K/yr

They will integrate cancer genomics, transcriptomics, imaging, and functional studies to identify ... spatial biology, or transcriptomic analyses. • Experience with computational analysis of genomic ...

All JobsComputational Spatial Transcriptomics JobsComputational Spatial Transcriptomics Jobs in Houston, TX

Computational Spatial Transcriptomics information

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$39

$52

$70

How much do computational spatial transcriptomics jobs pay per hour?

As of Aug 5, 2026, the average hourly pay for computational spatial transcriptomics in Houston, TX is $52.45, according to ZipRecruiter salary data. Most workers in this role earn between $44.76 and $70.24 per hour, depending on experience, location, and employer.

What are some typical challenges faced when working in computational spatial transcriptomics, and how can new team members prepare for them?

Professionals in computational spatial transcriptomics often encounter challenges related to handling and analyzing large, complex datasets that combine spatial and gene expression information. Integrating data from different technologies and ensuring data quality can be demanding, requiring strong programming skills and familiarity with bioinformatics pipelines. New team members can prepare by strengthening their skills in statistical analysis, programming languages like Python or R, and staying updated on the latest spatial transcriptomics techniques. Collaborating closely with experimental biologists and data scientists is also key to overcoming these challenges and driving successful research outcomes.

What is the difference between Computational Spatial Transcriptomics vs Computational Biologist?

AspectComputational Spatial TranscriptomicsComputational Biologist
Required CredentialsAdvanced degrees in bioinformatics, computational biology, or related fields; experience with spatial data analysisTypically a PhD or Master's in biology, bioinformatics, or related disciplines; strong programming skills
Work EnvironmentResearch labs, biotech companies, academic institutions focusing on spatial genomicsResearch institutions, biotech firms, academia working on biological data analysis
Industry UsageSpecialized in spatial transcriptomics techniques and data interpretationBroad biological data analysis across various fields

Computational Spatial Transcriptomics focuses on analyzing spatial gene expression data within tissues, requiring specialized skills in spatial data processing. In contrast, Computational Biologists work on a wider range of biological data types. While both roles involve bioinformatics expertise, the former emphasizes spatial data analysis techniques specific to transcriptomics.

What is computational spatial transcriptomics?

Computational spatial transcriptomics is a field that combines advanced computational methods with spatial transcriptomics, a technique that measures gene expression within the physical context of tissue samples. It involves processing and analyzing large datasets to map where specific genes are active within tissues, helping researchers understand how cells interact and function in their native environments. This approach is crucial for studies in developmental biology, cancer research, and neuroscience, as it provides insights into cellular organization and tissue architecture. Computational tools help extract meaningful patterns from complex data, enabling discoveries that were previously impossible with traditional methods.

What are the key skills and qualifications needed to thrive as a computational spatial transcriptomics scientist, and why are they important?

To excel in Computational Spatial Transcriptomics, you need a strong background in bioinformatics, genomics, and statistical data analysis, typically supported by advanced degrees in computational biology or related fields. Familiarity with programming languages (such as R and Python), spatial transcriptomics platforms (like 10x Genomics Visium), and high-throughput sequencing data analysis tools is essential. Strong problem-solving skills, attention to detail, and effective communication are crucial soft skills for interpreting complex datasets and collaborating with multidisciplinary teams. These competencies ensure accurate data interpretation, innovative research, and successful integration of spatial transcriptomics insights into biological and clinical applications.
What are popular job titles related to Computational Spatial Transcriptomics jobs in Houston, TX? For Computational Spatial Transcriptomics jobs in Houston, TX, the most frequently searched job titles are:
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Computational Spatial Transcriptomics jobs near you
Infographic showing various Computational Spatial Transcriptomics job openings in Houston, TX as of June 2026, with employment types broken down into 76% Full Time, 9% Part Time, 6% Temporary, and 9% Contract. Highlights an 81% Physical, 1% Hybrid, and 18% Remote job distribution, with an average salary of $109,105 per year, or $52.5 per hour.

Postdoctoral Fellow PhD - Urology

Baylor College of Medicine

Houston, TX • On-site

$46K - $63K/yr

Full-time

Posted yesterday

New

Baylor College of Medicine rating

8.0

Company rating: 8.0 out of 10

Based on 24 frontline employees who took The Breakroom Quiz

183rd of 614 rated colleges and universities

Job description

Summary

The Jorgez Laboratory in the Department of Urology at Baylor College of Medicine is seeking a highly motivated Postdoctoral Fellow PhD to join an NIH-funded research program investigating the molecular and developmental mechanisms underlying testicular dysfunction and infertility in a Wnt4 conditional knockout (Wnt4-cKO) mouse model. This position offers the opportunity to work at the interface of developmental biology, reproductive endocrinology, and cutting-edge spatial and 3D imaging technologies, within a collaborative, well-resourced research environment in the Texas Medical Center.

Baylor College of Medicine typically follows similar to the NIH stipulated stipend guidelines for Postdoctoral Associates.

Job Duties
  • Designs and executes experiments using mouse models that includes breeding colony management and genotyping.
  • Performs advanced 3D and spatial imaging (confocal, LSFM, Xenium spatial transcriptomics) and associated computational image analysis.
  • Conducts cellular and molecular assays including, immunohistochemistry, proteomic profiling, and tissue-derived cell culture-based functional studies.
  • Analyzes, interprets, and presents complex multimodal datasets integrating imaging, transcriptomic, and proteomic data.
  • Prepares manuscripts for peer-reviewed publication and contribute to grant progress reports and future funding applications.
  • Maintains reagents and supply for experiments.
  • Teaches techniques, procedures, and experimental design to other members of the lab.
  • Communicates research results in group meetings, national or international conferences.
  • Plans, directs, and conducts research experiments.
  • Develops research techniques and perform applications required for specific research projects
  • Perform other job-related duties as assigned.
Minimum Qualifications
  • Ph.D. in Basic Science, Health Science, or a related field.
  • No experience required.
Preferred Qualifications
  • Experience with advanced 3D imaging modalities (light sheet fluorescence microscopy, confocal microscopy) and/or spatial transcriptomics platforms (e.g., Xenium, Visium).
  • Background in male reproductive biology, testicular development, or endocrinology.
  • Experience with bioinformatic analysis of transcriptomic or proteomic datasets.
  • Experience with animal surgical techniques (e.g., orchidopexy models).
  • Experience working on the Wnt/Bcatenin signaling pathway.

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