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Transcriptomics Jobs in Dallas, TX (NOW HIRING)

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Transcriptomics information

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$48.5K

$201.3K

$395.7K

How much do transcriptomics jobs pay per year?

As of Aug 12, 2026, the average yearly pay for transcriptomics in Dallas, TX is $201,277.00, according to ZipRecruiter salary data. Most workers in this role earn between $77,700.00 and $395,700.00 per year, depending on experience, location, and employer.

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

To thrive as a Transcriptomics Scientist, you need a strong background in molecular biology, genomics, and bioinformatics, typically supported by an advanced degree in a relevant field. Familiarity with next-generation sequencing (NGS) platforms, RNA-seq analysis pipelines, and programming languages like R or Python is essential. Attention to detail, problem-solving abilities, and effective communication skills set outstanding candidates apart. These competencies are vital for generating accurate transcriptomic data, interpreting complex results, and collaborating within multidisciplinary research teams.

What is transcriptomics?

Transcriptomics is the study of the complete set of RNA transcripts produced by the genome under specific circumstances or in a specific cell. It provides insights into gene expression patterns, how genes are regulated, and how cells respond to various conditions. By analyzing the transcriptome, researchers can better understand biological processes, disease mechanisms, and identify potential targets for therapy. Technologies such as RNA sequencing (RNA-seq) are commonly used in transcriptomics research.

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

Professionals in transcriptomics frequently encounter challenges such as handling large and complex datasets, ensuring data quality, and staying current with rapidly evolving analytical tools and technologies. Working closely with bioinformaticians and statisticians is essential for effective data analysis and interpretation. Additionally, clear communication and collaboration with wet-lab biologists and clinicians help bridge the gap between raw data and meaningful biological insights. Regular training and professional development can help transcriptomics professionals stay updated with the latest best practices and software advancements.

What is the difference between Transcriptomics vs Bioinformatics?

AspectTranscriptomicsBioinformatics
Required credentialsBachelor's or Master's in Biology, Genetics, or related fields; experience with sequencing technologiesBachelor's or Master's in Computer Science, Bioinformatics, or related fields; programming skills
Work environmentLaboratories, research institutions, biotech companiesResearch labs, biotech firms, academic institutions, data analysis centers
Industry usageGenomics, molecular biology, medical researchData analysis, software development, computational biology

While both Transcriptomics and Bioinformatics involve analyzing biological data, Transcriptomics focuses on studying gene expression profiles using sequencing technologies, whereas Bioinformatics encompasses a broader range of computational methods to analyze various biological datasets. Professionals in both fields often collaborate but have distinct skill sets and work environments.

What are popular job titles related to Transcriptomics jobs in Dallas, TX? For Transcriptomics jobs in Dallas, TX, the most frequently searched job titles are:
What job categories do people searching Transcriptomics jobs in Dallas, TX look for? The top searched job categories for Transcriptomics jobs in Dallas, TX are:
What cities near Dallas, TX are hiring for Transcriptomics jobs? Cities near Dallas, TX with the most Transcriptomics job openings:
Transcriptomics jobs near you
Infographic showing various Transcriptomics job openings in Dallas, TX as of August 2026, with employment types broken down into 94% Full Time, and 6% Part Time. Highlights an 100% In-person job distribution, with an average salary of $201,277 per year, or $96.8 per hour.

POSTDOCTORAL RESEARCHER-Biomedical Data Science & AI-Epidemiology-Ruan Lab [Req#: 963199, Position#:

UT Southwestern Medical Center

Dallas, TX • On-site

Full-time

Posted 8 days ago

UT Southwestern rating

7.9

Company rating: 7.9 out of 10

Based on 152 frontline employees who took The Breakroom Quiz

108th of 887 rated healthcare providers

Job description

Description
POSTDOCTORAL RESEARCHER
A postdoctoral fellow position is now available in the laboratories of Dr. Peifeng Ruan (https://qbrc.swmed.edu/labs/ruanlab/) at the Quantitative Biomedical Research Center (QBRC) in the Peter O'Donnell Jr. School of Public Health at UT Southwestern Medical Center, Dallas. The labs' research focuses on developing and applying advanced computational and AI methods to address critical challenges in biomedical research and clinical practice.
The training activities of this position will include data management, curation, and analysis of large-scale biomedical and clinical datasets, including proteomics, metabolomics, single-cell transcriptomics and spatial transcriptomics data; development and/or implementation of novel statistics, machine learning and AI approaches; integration of molecular, cellular, spatial, imaging, and clinical data; interpretation and presentation of analysis results; and writing manuscripts. We are actively seeking exceptionally motivated individuals with outstanding problem-solving abilities. In this role, you will conduct cutting-edge research at the intersection of artificial intelligence and biomedical sciences, with opportunities to collaborate with clinical investigators and industry partners.
Information on our postdoctoral training program, benefits, and a virtual tour can be found at https://www.utsouthwestern.edu/research/postdoctoral-scholars/.
Qualifications
Candidates with a Ph.D. in computer science, bioinformatics, data science, machine learning, or a related quantitative field are encouraged to apply. Experience with one or more of the following areas is desirable: single-cell transcriptomics and spatial transcriptomics, knowledge graphs, large language models, or analysis of real-world clinical data (e.g., electronic health records, claims databases, or pharmacovigilance databases).
Application Instructions
Interested individuals should send a CV and a list of three references to:
Peifeng Ruan
Peifeng.Ruan@UTSouthwestern.edu

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