This job has expired

Postdoctoral Associate

UPMC Hillman Cancer Center - University of Pittsburgh
Pittsburgh, Pennsylvania
Salary based on experience plus excellent benefits.
Closing date
Mar 1, 2023

View more

Life Sciences, Computational Biology
Position Type
Full Time
Job Type
Organization Type

NIH-funded Postdoctoral Positions Available in Computational Biology and Machine Learning


Postdoctoral research positions are immediately available in our research group ( to work at the intersection of single cell genomics and machine learning. We are looking for two types of candidates: those interested to develop and apply methods (e.g interpretable deep learning for single cell multi-omics data integration) and those interested to apply and enhance methods for delineating cell context-specific regulatory programs for precision medicine. The postdoctoral researcher will be working clinically important questions in cancer and immunology. Postdocs will engage with the broader systems biology communities by presenting work at top conferences, as well as publishing applications of new methods in high-impact journals.



  • The Osmanbeyoglu Lab is a multi-disciplinary hybrid wet/dry lab at the University of Pittsburgh. We are affiliated with the Department of Biomedical Informatics, Bioengineering, Biostatistics, the Center for Systems Immunology, and UPMC Hillman Cancer Center
  • Our projects are funded by NCI, NIGMS and The Fund for Innovation in Cancer Informatics
  • The University consistently ranks in the top 5 nationally for NIH biomedical research funding and the Cancer Center was recently ranked #7 by US News & World Report
  • Candidates are eligible to apply for the enhanced stipend and career development funding provided as a Hillman Postdoctoral Fellow for Innovative Cancer Research (details provided at ).


For more information on the laboratory and its research, please see the following publications and our website ( 

  1. Tao Y*, Ma X*, Palmer D, Schwartz R, Lu X, Osmanbeyoglu HU. Interpretable deep learning for chromatin-informed inference of transcriptional programs driven by somatic alterations across cancers Nucleic Acids Research, 2022; gkac881,
  2. Ma X*, Somasundaram A*, Qi Z, Hartman D, Singh H, Osmanbeyoglu HU (2021) SPaRTAN, a computational framework for linking cell-surface receptors to transcriptional regulators. Nucleic Acids Research, Volume 49, Issue 17, 27 September 2021, Pages 9633–9647,


QUALIFICATIONS: PhD in applied quantitative discipline, such as computational biology, bioinformatics, biostatistics, statistics, mathematics, computer science or engineering with a strong interest in translational biomedical research. Ideal candidates would have publications demonstrating experience with code development, applied mathematics, statistics, machine learning, deep learning and/or computational biology. The candidate should 1) be able to work independently and as a member of a team, and 2) be hard-working, motivated, and eager to learn with an outstanding work ethic. 


TO APPLY: If interested, please send an email to Dr. Osmanbeyoglu (osmanbeyogluhu @ pitt dot edu) and include your CV (with references) and research interests. Please put the words “POSTDOC-APPLICATION-2023” in the subject line.  


Benefits: standard employee with outstanding health benefits

Get job alerts

Create a job alert and receive personalized job recommendations straight to your inbox.

Create alert