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 -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.