A postdoctoral position is available in the Wang Laboratory at UPMC Hillman Cancer Center. We seek highly motivated scientists with expertise in computational genomics/AI and/or translational cancer biology to join our dynamic, well-funded research program at the frontier of cancer genetics and precision oncology.

Candidates with training in both computational genomics and cancer biology are especially welcome to apply—our lab thrives on bridging computational discovery with experimental validation, and dual-skilled scientists will find exceptional opportunities to lead integrative projects spanning both domains.

Our lab operates at the intersection of computational innovation and experimental cancer biology. Funded by over $11.5 million in research grants—including four active DOD Breakthrough Awards totaling $5.1 million—we offer an exceptional environment for ambitious postdoctoral scientists to make high-impact discoveries with direct clinical translational potential.

Why Join Us?

"Dark matter" cancer genetics: Pioneer discoveries in uncharted areas of breast cancer genetics, including recurrent gene fusions (ESR1-CCDC170, BCL2L14-ETV6, RAD51AP1-DYRK4) and intragenic rearrangements (IGRs)—a largely unexplored class of genetic aberrations.

Precision immuno-oncology: Develop next-generation biomarkers (IGR burden, TAA burden, IMPREG signature) for immunotherapy patient selection, especially for TMB-low and PD-L1-negative cancers where current tools fall short.

AI-powered precision oncology: Build mechanism-driven AI and agentic AI frameworks (iGenSig-AI, G2K) that integrate biological knowledge with cutting-edge machine learning to transform omics data into actionable therapeutic insights.

Translational impact: Work alongside oncologists on a rapid discovery-to-clinic pipeline, with prospective clinical study and clinical trial design directly linked to laboratory findings.

Proven trainee success: Our postdoctoral alumni have received prestigious fellowships from the DOD, Susan G. Komen Foundation, Hillman Cancer Center, and the Gottfried Family Women's Health Award, totaling over $1.3M in trainee funding.

High-impact publications: Join a track record of publications in Nature Biotechnology, Nature Communications, Science, PNAS, Cancer Discovery, Cancer Research, Cancer Immunology Research, and Clinical Cancer Research.

Research Area 1: Computational Genomics & AI-Driven Precision Oncology

This position focuses on developing and applying advanced computational and AI methods to tackle major challenges in cancer genomics and precision medicine. Specific areas include:

1) Building the Genomics to Knowledge (G2K) agentic AI framework for automated transformation of multi-omics data into biological insights through iterative, hypothesis-driven computational analysis. 2) Characterizing the landscape of structural mutations—including intragenic rearrangements (IGRs)—across cancer types and modeling their impact on the tumor immune microenvironment and immunotherapy response. 3) Developing clinical-grade mechanism-driven AI models (iGenSig-AI) for predicting responses to targeted therapies and immunotherapies, integrating graph neural networks, regulon-aware pooling, and transfer learning with biological regulatory networks. 4) Developing and validating computational biomarkers (IGR burden, TAA burden, IMPREG signature) for precision immuno-oncology panels.

Research Area 2: Translational Cancer Biology & Immunobiology

This position focuses on the experimental validation and biological characterization of newly discovered genetic targets at the interface of cancer genetics, pathobiology, and immunobiology. Specific areas include:

1) Investigating the functional roles of recurrent gene fusions (ESR1-CCDC170, BCL2L14-ETV6, RAD51AP1-DYRK4) and novel intragenic rearrangements in breast and ovarian cancer progression, immune evasion, and therapy resistance. 2) Characterizing novel structural mutations in actionable kinases and evaluating genotype-directed therapeutic strategies in preclinical models. 3) Performing in vitro and in vivo validation of computationally predicted cancer targets, including studies of epithelial-mesenchymal transition, drug resistance, and immune dysfunction. 4) Exploring immunotherapeutic strategies guided by biomarker status, including combination therapies with β-catenin inhibitors and immune checkpoint blockade in triple-negative breast cancer.

Qualifications:

Ph.D. in bioinformatics, computational biology, computer science, cancer biology, molecular biology, immunology, genetics, or a related field. Depending on research focus, relevant experience may include machine learning, multi-omics data analysis, cancer genomics, immunogenomics, or systems biology of transcriptional regulation (strong programming skills in Python, R, or equivalent expected), and/or cell and molecular biology techniques, animal models, immunology assays, or translational research. Candidates with combined computational and experimental skills are especially encouraged to apply.

The University of Pittsburgh is an equal opportunity employer / disability / veteran.