Postdoctoral Associate position is available in the laboratory of Dr. Yu-Chiao Chiu at the Department of Medicine and UPMC Hillman Cancer Center at the University of Pittsburgh School of Medicine. We are actively funded by multiple federal and private agencies, including the NIH (NCI R00), Hillman Developmental Pilot Program (NCI P30), Pittsburgh Liver Research Center (NIDDK P30), and Leukemia Research Foundation. The candidate will contribute to these projects to systematically understand the biology of adult and pediatric cancers, to identify diagnostic and prognostic biomarkers, and to improve cancer therapy. Potential research topics include deep learning applications to 1) spatial single cell profiles, 2) literature data using large language models (LLMs), and 3) multi-modal genomics and pharmacogenomics data. At the UPMC Hillman Cancer Center, the candidate will have the opportunity to build cross-disciplinary collaborations with clinical, translational, and basic scientists in order to bridge cutting-edge computational algorithms to unmet needs in precision oncology.

The Chiu Lab focuses on the development of state-of-the-art machine and deep learning models that integrate multi-modal genomic and pharmacogenomic data to study cancer biology and improve cancer therapy. Our latest works are well-recognized by broad cancer and bioinformatics communities, which include publications in Science Advances (selected by @NCIgenomics as the #1 paper), Bioinformatics Advances, Briefings in Bioinformatics, and BMC Medical Genomics (selected as Springer Nature Highlight in Genetics), as well as frequent presentations in the AACR, ISMB, and GLBIO conferences. Please visit our lab website www.chiu-lab.org/ for more information.

Qualifications or Expectations:

• Experience in deep learning, machine learning, single-cell and spatial omics, natural language processing, large language models, and/or image processing is strongly desired.
• Highly motivated scientists who have recently earned a Ph.D. degree in bioinformatics, computational biology, electrical/computer/biomedical engineering, computer science, or a related field.
• Must be proficient with Linux and multiple bioinformatics programming languages, such as Python, R, MATLAB, and Perl.
• Must possess excellent written and verbal English communication skills.
• Strong experience in computational modeling of biological systems, large-scale cancer multi-omic datasets (TCGA, HTAN, TARGET, CCLE, etc.), high-throughput drug and genetic screens (DepMap, PRISM, GDSC, etc.), and common bioinformatics resources (NCBI, UCSC, Ensembl, etc.) is essential.

Opportunities Available:

• Candidates will receive dedicated mentorship on career development planning, career development awards, and postdoctoral fellowships such as the Hillman Postdoctoral Fellowships for Innovative Cancer Research (https://hillmanresearch.upmc.edu/research/hillman-fellows/postdoctoral/).
• Our lab members have cross-disciplinary experience spanning across engineering, informatics, biology, and medicine.
• The PI has successful experience in acquiring the prestigious NIH/NCI K99/R00 Pathway to Independence Award and postdoctoral fellowships, as well as mentoring F99/K00 predoc to postdoc transition award application.

Duties or Responsibilities:

• Conduct postdoctoral research to systematically understand the biology of adult and pediatric cancers, to identify diagnostic and prognostic biomarkers, and to improve cancer therapy. Potential research topics include deep learning applications to 1) spatial single cell profiles, 2) literature data using large language models (LLMs), and 3) multi-modal genomics and pharmacogenomics data.

For more info about this position, visit:https://cfopitt.taleo.net/careersection/pitt_faculty_external_pd/jobdetail.ftl?job=22005468

Application Instructions:

Visit join.pitt.edu and View Requisition Number #22005468 to apply. Please submit curriculum vitae, one-page research statement, and contact information of three references. Review of applications will start immediately and continue on a rolling basis until the position is filled.