Exciting postdoctoral research opportunities are currently open within our dynamic research group at the intersection of single-cell genomics and machine learning. Explore the cutting-edge realm of bioinformatics and computational biology by joining us at osmanbeyoglulab.com. We are seeking two types of candidates to contribute to groundbreaking projects:
Method Development Enthusiasts:
Dive into the development and application of advanced methodologies, including interpretable deep learning tailored for single-cell multi-omics data integration.
Work on the forefront of technology, pushing the boundaries of what is possible in data analysis and interpretation.
Precision Medicine Pioneers:
Contribute to the application and enhancement of methods focused on delineating cell context-specific regulatory programs.
Tackle clinically relevant questions in the fields of cancer and immunology, shaping the future of precision medicine.
ENVIRONMENT:
• The Osmanbeyoglu Lab is a computational omics 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, and NIGMS
• The University consistently ranks in the top 3 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 hillmanresearch.upmc.edu/research/hillman-fellows/postdoctoral/ ).
For more information on the laboratory and its research, please see the following publications and our website (osmanbeyoglulab.com):
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, doi.org/10.1093
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, doi.org/10.1093/nar/gkab745
Ideal Candidate:
Possesses a strong background in machine learning and is eager to gain domain-specific experience in the fields of cancer and immunology.
Thrives in a collaborative and interdisciplinary research environment, demonstrating a passion for pushing the boundaries of current scientific understanding.
This opportunity provides a unique platform for machine learning expert to broaden their horizons and contribute meaningfully to the field of systems biology. If you are driven by innovation and ready to make a significant impact, we encourage you to explore this position with us. Join us in pushing the boundaries of scientific discovery and application. Apply now and be part of a transformative research journey at the forefront of computational biology.
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-2024” in the subject line. Individuals from underrepresented minorities or disadvantaged backgrounds are particularly encouraged to apply.
Benefits: standard employee with outstanding health benefits