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Metabolomics Postdoctoral Fellow Jobs (NOW HIRING)

Marylandconnect

$47K - $64K/yr

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Postdoctoral/research fellow will responsible for coordinating all Plant metabolomics/Hydroponics and Aquaponics research and hands-on laboratory activities associated with Undergraduate Research ...

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Metabolomics Postdoctoral Fellow information

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$25K

$59K

$83.5K

How much do metabolomics postdoctoral fellow jobs pay per year?

As of Aug 18, 2026, the average yearly pay for metabolomics postdoctoral fellow in the United States is $59,022.00, according to ZipRecruiter salary data. Most workers in this role earn between $49,000.00 and $66,500.00 per year, depending on experience, location, and employer.

What is a metabolomics postdoctoral fellow?

A Metabolomics Postdoctoral Fellow is a researcher who has recently earned their Ph.D. and is engaged in advanced study and research in the field of metabolomics. Metabolomics is the large-scale study of small molecules, known as metabolites, within cells, biofluids, tissues, or organisms. Postdoctoral fellows in this area typically design and conduct experiments, analyze complex data sets, and collaborate with other scientists to understand metabolic pathways and their roles in health and disease. Their work often supports biomedical, pharmaceutical, or agricultural research initiatives.

What are the key skills and qualifications needed to thrive as a metabolomics postdoctoral fellow?

To thrive as a Metabolomics Postdoctoral Fellow, you need advanced expertise in biochemistry, analytical chemistry, and metabolomics, usually supported by a PhD in a relevant field. Familiarity with mass spectrometry, chromatography techniques, and bioinformatics tools such as MetaboAnalyst is highly valued. Strong problem-solving skills, scientific communication, and the ability to collaborate effectively with multidisciplinary teams set exceptional candidates apart. These skills enable rigorous experimental design, accurate data interpretation, and impactful scientific contributions in metabolomics research.

What are some of the biggest challenges metabolomics postdoctoral fellows face when managing large-scale datasets?

Metabolomics Postdoctoral Fellows often encounter challenges when handling vast and complex datasets, including ensuring data quality, managing batch effects, and integrating multi-omics data. Effective data processing requires familiarity with advanced bioinformatics tools and careful experimental design to maintain reproducibility and reliability. Collaborating with statisticians, bioinformaticians, and other researchers is essential to overcome these hurdles and to interpret results accurately, which enhances the overall impact of the research.

What is the difference between Metabolomics Postdoctoral Fellow vs Biochemist?

AspectMetabolomics Postdoctoral FellowBiochemist
Required CredentialsPh.D. in Biochemistry, Chemistry, or related fieldBachelor's or Master's in Biochemistry, Chemistry, or related field; Ph.D. preferred for advanced roles
Work EnvironmentResearch laboratories, academic institutions, biotech companiesLaboratories, research institutions, pharmaceutical companies
Industry UsageAcademic research, postdoctoral training, specialized metabolomics projectsResearch and development, product development, quality control

The Metabolomics Postdoctoral Fellow focuses on advanced research in metabolomics, often within academic or specialized settings, while a Biochemist has a broader role in biochemical research and development. Both roles require strong laboratory skills, but the fellow typically engages in postdoctoral training with a focus on metabolomics techniques and data analysis.

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Metabolomics Postdoctoral Fellow jobs near you
Infographic showing various Metabolomics Postdoctoral Fellow job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 76% Full Time, 22% Part Time, and 1% Contract. Highlights an 90% Physical, and 10% Remote job distribution, with an average salary of $59,022 per year, or $28.4 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 13 days ago

UT Southwestern rating

7.9

Company rating: 7.9 out of 10

Based on 152 frontline employees who took The Breakroom Quiz

107th of 888 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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