Post Doctoral Associate at UPMC

Pittsburgh, Pennsylvania, United States -

Full Time

Start Date

Immediate

Expiry Date

18 Apr, 26

Salary

0.0

Posted On

18 Jan, 26

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Bioinformatics, Computational Biology, Computer Science, Electrical Engineering, Machine Learning, Deep Learning, Large Language Models, Multi-Omics Datasets, Single-Cell Datasets, Spatial Omics, Programming, Linux, Python, R, MATLAB, Perl

Industry

Hospitals and Health Care

Description

A Postdoctoral Associate position in AI/Cancer Research, funded by an NIH/NIGMS R35 grant and intramural sources, is available in the laboratory of Dr. Yu-Chiao Chiu at the Department of Medicine (Division of Malignant Hematology/Medical Oncology) and UPMC Hillman Cancer Center at the University of Pittsburgh. The candidate will engage in bioinformatics research aimed at systematically understanding cancer biology, identifying diagnostic and prognostic biomarkers, and improving cancer therapy. The candidate will develop AI solutions (machine learning, deep learning, and large language models) for high-throughput multi-omics datasets (particularly single-cell and spatial omics) and large textual corpora (e.g., scientific literature). We are seeking a highly motivated and enthusiastic individual who has recently earned a Ph.D. degree in bioinformatics, computational biology, computer science, or electrical engineering. Strong experience integrating multiple of the following components is required: computational modeling of molecular biology (particularly deep learning and large language models), large-scale cancer multi-omic datasets (TCGA, TARGET, CCLE, etc.), single-cell and spatial transcriptomic/proteomic datasets, high-throughput drug and genetic screens (DepMap, PRISM, etc.), and common bioinformatics resources (NCBI, UCSC, Ensembl, etc.). Proficiency with Linux and programming in multiple bioinformatics-relevant languages (such as Python, R, MATLAB, and Perl) is also required. Effective written and verbal communication skills and a collaborative mindset are essential. The Chiu Lab (https://chiu-lab.org/) focuses on developing state-of-the-art multimodal AI models for cancer research. Our recent publications have been recognized by both the cancer research and bioinformatics communities, including in Cancer Research Communications (doi:10.1158/2767-9764.CRC-25-0030; presented and highlighted at the AACR Annual Meeting 2025) and Science Advances (doi:10.1126/sciadv.abh1275; highlighted by @NCIgenomics as the #1 favorite paper of the year). The Chiu Lab is actively supported by funding from the NIH (NIGMS, NCI, and OD), private foundations such as the Leukemia Research Foundation, and institutional funds. Dr. Chiu has a proven track record of securing prestigious career development awards such as the NIH/NCI K99/R00 Pathway to Independence Award and mentoring recipients of the NIH/NCI F30 Predoctoral MD/PhD Fellowships. The selected candidate will receive personalized mentorship in career development planning, competitive award applications, and other resources available through the UPMC Hillman Cancer Center (https://hillmanresearch.upmc.edu/research/hillman-fellows/postdoctoral/). Opportunities for cross-disciplinary collaboration with clinical, translational, and basic scientists will enable the candidate to apply cutting-edge computational approaches to real-world challenges in precision oncology. The University of Pittsburgh School of Medicine is ranked #6 in NIH funding and a Tier 1 institution in Best Medical Schools: Research. Pittsburgh is consistently ranked among the most livable U.S. cities. Interested candidates should submit their curriculum vitae, a one-page summary of research experience and interests, and contact information for three references.

Responsibilities

The candidate will engage in bioinformatics research to understand cancer biology and improve cancer therapy. They will develop AI solutions for high-throughput multi-omics datasets and large textual corpora.