Postdoctoral Researcher in AI, and Gene Regulation

Job Title: Postdoctoral Researcher in AI, and Gene Regulation

Job Number: 88544

Location: Pittsburgh, US

Job Description

The Osmanbeyoglu Lab is currently seeking postdoctoral researchers to join our team at the intersection of single-cell and spatial genomics and machine learning. For more details, please visit https://www.osmanbeyoglulab.com/.

We are looking for candidates with two main areas of interest:

Method Development and Application: Candidates interested in developing and applying methods such as interpretable deep learning for single-cell and spatial genomics.
Cell Context-Specific Regulatory Programs: Candidates passionate about enhancing methods for understanding cell context-specific regulatory programs.

About the Position: Postdoctoral researchers will address clinically significant questions in cancer, liver diseases and immunology. Opportunities for engagement with broader systems biology communities through presentations at top conferences and publications in high-impact journals are encouraged.

Our Lab Environment: The Osmanbeyoglu Lab is a computational omics lab at the University of Pittsburgh, affiliated with multiple departments and centers, including Biomedical Informatics, Bioengineering, Biostatistics, the Center for Systems Immunology, and UPMC Hillman Cancer Center. Our projects are funded by the NCI, NIGMS, and The Fund for Innovation in Cancer Informatics.

The University of Pittsburgh consistently ranks in the top 5 for NIH biomedical research funding, and the Hillman Cancer Center is ranked #7 by US News & World Report.
Candidates are eligible to apply for enhanced stipends and career development funding as Hillman Postdoctoral Fellows for Innovative Cancer Research.

Benefits: Compensation above NIH guidelines, commensurate with experience and education.
Standard employee benefits, including comprehensive health insurance coverage.

Qualifications: Candidates should have a PhD in an applied quantitative discipline, such as computational biology, bioinformatics, biostatistics, mathematics, or computer science, with a strong interest in translational biomedical research.
Ideal candidates should have demonstrated experience in code development, applied mathematics, machine learning, deep learning, and/or computational biology through publications.
The successful candidate should be capable of working independently and collaboratively, exhibiting diligence, motivation, and a strong work ethic.
If you are passionate about cutting-edge biomedical research and eager to contribute to impactful cancer and immunology studies, we encourage you to apply.

Application Deadline: 2025-05-16