Company:  Confidential

Job Title: Postdoctoral Positions Available for Single-Cell Omics and Cancer Genomics

Job Number: 78830

Location: Pittsburgh, US

Job Description

Immediate openings are available in our research group at the intersection of single-cell and spatial genomics and machine learning. We’re looking for candidates who have one or both of the following primary interests:

Method Development: Individuals interested in developing and applying methods, such as interpretable deep learning for single-cell and spatial genomics.
Context-Specific Regulatory Programs: Those interested in applying and enhancing methods for delineating cell context-specific regulatory programs.

About the Position: Postdoctoral researchers will tackle clinically important questions in cancer and immunology.
Engagement with broader systems biology communities through presentations at top conferences and publications in high-impact journals is encouraged.

Environment: The Osmanbeyoglu Lab is a multi-disciplinary lab at the University of Pittsburgh, affiliated with various departments and centers including Biomedical Informatics, Bioengineering, Biostatistics, the Center for Systems Immunology, and UPMC Hillman Cancer Center.
Our projects receive funding from NCI, NIGMS, and The Fund for Innovation in Cancer Informatics.
The University consistently ranks in the top 5 nationally for NIH biomedical research funding, with the Cancer Center recently ranked #7 by US News & World Report.
Candidates can apply for enhanced stipends and career development funding provided as a Hillman Postdoctoral Fellow for Innovative Cancer Research. Details available at Hillman Research.

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

Qualifications: Candidates should possess 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 would have publications demonstrating experience with applied mathematics, statistics, machine learning, deep learning, or computational biology. The successful candidate should be capable of working both independently and as part of a team, exhibiting qualities of diligence, motivation, and eagerness to learn, alongside an outstanding work ethic. 

Application Deadline: 2024-11-12

 

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