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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 ... spatial biology, or transcriptomic analyses. • Experience with computational analysis of genomic ...

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

$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 ...

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

Computational Spatial Transcriptomics information

See Houston, TX salary details

$39

$52

$70

How much do computational spatial transcriptomics jobs pay per hour?

As of Jul 4, 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:
What job categories do people searching Computational Spatial Transcriptomics jobs in Houston, TX look for? The top searched job categories for Computational Spatial Transcriptomics jobs in Houston, TX are:
Computational Spatial Transcriptomics jobs near you
Postdoctoral Associate

Postdoctoral Associate

Baylor College of Medicine

Houston, TX

Full-time

Posted 6 days ago

Baylor College of Medicine rating

8.6

Company rating: 8.6 out of 10

Based on 21 frontline employees who took The Breakroom Quiz

54th of 544 rated colleges and universities

Job description

Summary

The Cheng Lab in the Department of Medicine, Section of Epidemiology & Population Sciences is searching for highly motivated and talented Postdoctoral Associate to work on Bioinformatics and Computational Biology in Cancer Genomics and Immunology.  This position will be involved in the development and/or application of computational approaches to understand the mechanism of cancer development, progression, metastasis, and prognosis.  The Postdoc should have experience in processing and analyzing data from the next-generation sequencing and single-cell technologies (e.g., scRNA-seq, scATAC-seq, scTR-seq, and/or spatial transcriptomics). Previous experience in both Bioinformatics/Genomics and Cancer Biology is desirable. The Postdoc is expected to collaborate closely with experimental biologists and clinicians. The position offers an extraordinary team-based science environment with opportunities for significant education, training, and career development. The role includes designing analyses, interpreting complex datasets, and translating findings into biologically meaningful insights within a multidisciplinary research environment.  This position offers a highly collaborative, team-based setting with opportunities for advanced training, mentorship, and career development.

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

Job Duties
  • Develops and/or applies computational approaches to understand the mechanism of cancer development, progression, metastasis, and prognosis.
  • Collaborates closely with experimental biologists and clinicians to design studies, validates computational findings, and supports translational applications of research discoveries.
  • Participates in regular joint meetings to present findings, discuss ongoing projects, and align research strategies with lab and departmental priorities.
  • Assists in mentoring and training of junior researchers in computational techniques and bioinformatics best practices.
  • Translates complex computational results into biologically meaningful insights in collaboration with wet-lab teams.
  • Maintains thorough documentation of analyses, pipelines, and datasets in version-controlled environments.
  • Collaborates with experimental biologists and clinicians to apply computational and bioinformatics approaches to study cancer development, progression, metastasis, and prognosis.
  • Performs other job-related duties as assigned.
Minimum Qualifications
  • MD or Ph.D. in Basic Science, Health Science, or a related field.
  • No experience required.
Preferred Qualifications
  • PhD in Computational Biology, Bioinformatics, or a related field (e.g. statistics, computer science, or quantitative biology).
  • Knowledge of basic molecular biology, genomics, and epigenetics.
  • Experience in processing and analyzing data from the next-generation sequencing and single-cell technologies (e.g., scRNA-seq, scATAC-seq, scTR-seq, and/or spatial transcriptomics). 
  • Experience in both Bioinformatics/Genomics and Cancer Biology is desirable.
  • Experience in the application and development of computational methods/tools or machine learning algorithms.
  • Good computer programming skills in R/Matlab/PerlPython.

Baylor College of Medicine is an Equal Opportunity/Affirmative Action/Equal Access Employer.

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