Post Doctoral.Post Doctoral.Associate
About The Position
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.
Requirements
- Ph.D. in bioinformatics, computational biology, computer science, cancer biology, molecular biology, immunology, genetics, or a related field.
- 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.
- Experience with cell and molecular biology techniques, animal models, immunology assays, or translational research.
Nice To Haves
- Candidates with training in both computational genomics and cancer biology are especially welcome to apply.
- Dual-skilled scientists will find exceptional opportunities to lead integrative projects spanning both domains.
- Candidates with combined computational and experimental skills are especially encouraged to apply.
Responsibilities
- Pioneer discoveries in uncharted areas of breast cancer genetics, including recurrent gene fusions and intragenic rearrangements.
- Develop next-generation biomarkers for immunotherapy patient selection, especially for TMB-low and PD-L1-negative cancers.
- Build mechanism-driven AI and agentic AI frameworks that integrate biological knowledge with cutting-edge machine learning to transform omics data into actionable therapeutic insights.
- Work alongside oncologists on a rapid discovery-to-clinic pipeline, with prospective clinical study and clinical trial design directly linked to laboratory findings.
- Develop and apply advanced computational and AI methods to tackle major challenges in cancer genomics and precision medicine.
- Characterize the landscape of structural mutations across cancer types and model their impact on the tumor immune microenvironment and immunotherapy response.
- Develop clinical-grade mechanism-driven AI models for predicting responses to targeted therapies and immunotherapies.
- Develop and validate computational biomarkers for precision immuno-oncology panels.
- Investigate the functional roles of recurrent gene fusions and novel intragenic rearrangements in cancer progression, immune evasion, and therapy resistance.
- Characterize novel structural mutations in actionable kinases and evaluate genotype-directed therapeutic strategies in preclinical models.
- Perform in vitro and in vivo validation of computationally predicted cancer targets, including studies of epithelial-mesenchymal transition, drug resistance, and immune dysfunction.
- Explore immunotherapeutic strategies guided by biomarker status, including combination therapies with β-catenin inhibitors and immune checkpoint blockade in triple-negative breast cancer.
Benefits
- Funded by over $11.5 million in research grants
- Exceptional environment for ambitious postdoctoral scientists to make high-impact discoveries with direct clinical translational potential.
- Opportunities to pioneer discoveries in uncharted areas of breast cancer genetics.
- Opportunities to develop next-generation biomarkers for immunotherapy patient selection.
- Opportunities to build mechanism-driven AI and agentic AI frameworks.
- Opportunities to work alongside oncologists on a rapid discovery-to-clinic pipeline.
- Proven trainee success with prestigious fellowships and over $1.3M in trainee funding.
- Track record of high-impact publications in top-tier journals.
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What This Job Offers
Job Type
Full-time
Career Level
Entry Level
Education Level
Ph.D. or professional degree
