Post Doctoral.Associate

at  The University of Pittsburgh

POST DOCTORAL.ASSOCIATE

Med-Biomedical Informatics - Pennsylvania-Pittsburgh - (24004569)
Immediate opening is available in our research group at the intersection of single-cell and spatial genomics and machine learning. We’re looking for a candidate who has 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: The postdoctoral researcher 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 are 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.
The University of Pittsburgh is committed to championing all aspects of diversity, equity, inclusion, and accessibility within our community. This commitment is a fundamental value of the University and is crucial in helping us advance our mission, which includes attracting and retaining diverse workforces. We will continue to create and maintain an environment that allows individuals to discover, belong, contribute, and grow, while honoring the experiences, perspectives, and unique identities of all.
The University of Pittsburgh is an Affirmative Action/Equal Opportunity Employer and values equality of opportunity, human dignity and diversity. EOE, including disability/vets.

Please refer the Job description for details