Bioinformatics Scientist in AI, Single-Cell Omics, and Cancer Therapeutics

University of Pittsburgh School of Medicine
UPMC Hillman Cancer Center
United States PA Pittsburgh


A Bioinformatics Scientist 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 seeking a highly motivated candidate with strong computational skills in machine learning and large-scale multi-omics, as well as in-depth knowledge in cancer biology and medicine. The Bioinformatics Scientist will lead research projects on developing new bioinformatics algorithms and machine/deep learning methods for single-cell and other high-throughput omics data. The candidate will independently carry out bioinformatics analysis and interpretation of multiple data types, such as genomics, functional genomics, and genetic and pharmacologic screens. Responsibility will also include implementation of bioinformatics tools to foster data accessibility and utilization among the biomedical community.

At the UPMC Hillman Cancer Center, the candidate will work closely with clinical, translational, and basic cancer researchers to bridge cutting-edge computational algorithms to unmet needs in precision oncology. The candidate will additionally train lab members in basic bioinformatics programs and workflows, as well as assist with preparing scientific data for publications and grant proposals. The successful candidate must demonstrate strong ability to multitask among collaborative and independent research projects, work in a fast-paced and multidisciplinary team, and independently identify and solve problems.

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 publications are well-recognized by broad cancer and bioinformatics communities: Science Advances (selected by @NCIgenomics as the #1 paper of 2021), BMC Medical Genomics (selected as Springer Nature Research Highlight in Genetics of 2019), and Briefings in Bioinformatics. The lab is actively supported by NIH and intramural funds. Our ongoing NIH/NCI funded project focuses on deep learning-based prediction of drug sensitivity and genetic dependency of pediatric cancers. Please visit our lab website for more information.


• Master's degree with at least five years of relevant experience. Equivalent combination of education and experience may be substituted for the education/experience requirement.
• Strong experience with integrated analyses of high throughput ‘omics’ datasets (RNA-seq, whole exome sequencing, whole genome sequencing, etc.) is preferred.
• Strong experience in applying machine learning to multi-omics and public cancer datasets, such as TCGA, CCLE, GDSC, DepMap, cBioPortal, etc., is preferred.
• Proficiency in Python, R, Perl, and/or MATLAB, and shell scripting in a Linux environment is required. Experience in using high performance computing, Docker, and MySQL is preferred.
• Experience in single-cell imaging and/or omics, and in-depth knowledge in molecular and cancer biology are highly preferred.
• Track record of research and creativity as demonstrated by publications and conference presentations as a first/co-first author is highly preferred.
• Experience in a highly collaborative environment and a track record of published high-impact collaborative research is desirable.
• Strong skills in interpersonal/cross-disciplinary communication and scientific writing are essential.

Start date

As soon as possible

How to Apply

Interested candidates should apply via (Requisition #22009189 under “Staff Positions”). Review of applications will start immediately and continue on a rolling basis until the position is filled. Inquiries may be directed to Yu-Chiao Chiu, PhD, Assistant Professor ( Individuals from underrepresented minorities or disadvantaged backgrounds are particularly encouraged to apply.