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Postdoctoral Fellow
NIH- and ARPA-H-Funded Postdoctoral Positions in AI/Bioinformatics Full-time
NIH- and ARPA-H-Funded Postdoctoral Positions in AI/Bioinformatics for Multi Omics
University of Pittsburgh School of Medicine | Osmanbeyoglu Lab
The Osmanbeyoglu Lab, a computational omics research group at the University of Pittsburgh School of Medicine, is seeking highly motivated postdoctoral researchers to join our team at the forefront of integrating artificial intelligence and multi-omics to advance precision medicine.
These fully funded positions are supported by grants from the NIH/NIGMS/NCI, and ARPA-H. We are currently hiring for two distinct postdoctoral roles:
- Method Development Focus – Ideal for candidates with a strong AI/ML background interested in developing interpretable deep learning models for multi-omics data integration.
- Application & Discovery Focus – For those passionate about applying computational tools to uncover context-specific regulatory mechanisms and therapeutic targets in complex diseases.
Both positions involve:
- Addressing clinically meaningful questions
- Interpreting complex bulk, single-cell and spatial transcriptomics datasets
- Publishing in high-impact journals and presenting at top-tier conferences (e.g. ISMB, RECOMB, ECCB)
This is an exceptional opportunity for machine learning scientists seeking to gain domain-specific expertise in computational biology and contribute to cutting-edge cancer detection, and precision medicine research.
About the Osmanbeyoglu Lab:
A collaborative and innovative group focused on computational omics. Our affiliations include:
- Department of Biomedical Informatics (DBMI) — https://www.dbmi.pitt.edu/
- Center for Transcriptional Medicine (CTM) — https://ctm.pitt.edu/
Benefits:
Standard employee benefits including outstanding health coverage.
Qualifications:
- Ph.D. in a quantitative or computational field (ML, data science, computational biology, bioinformatics, biostatistics, computer science, etc.)
- Strong coding skills in Python, R, or MATLAB, with experience in Linux
- Background in deep learning, machine learning, or image analysis
- Exposure to biological data (especially single-cell or spatial omics) is a plus but not required
- Excellent written and verbal communication skills
- Independent, collaborative, motivated, and eager to make impactful discoveries
How to Apply:
Please email Dr. Osmanbeyoglu ([Please click the Apply button for the link or address]) with your CV (including references) and a brief statement of your research interests.
Subject line: POSTDOC-APPLICATION-2025