The Translational Immuno-Informatics Laboratory (TIILab; PI: Riyue Bao, https://www.tiilab.org/ ) at the University of Pittsburgh School of Medicine and UPMC Hillman Cancer Center is seeking a highly motivated computational postdoctoral scholar to lead data-driven studies of resistance to cancer immunotherapy. TIILab is a multidisciplinary computational and translational research group focused on discovering mechanisms of treatment response and resistance directly from patient data and translating these findings into experimentally testable and clinically relevant strategies. The laboratory has multiple R01- and DoD-funded programs spanning lung, head and neck, melanoma, and liver cancers, with established patient cohorts, multimodal datasets, and experimental model systems.

The candidate will lead independent and collaborative projects using multimodal, clinically annotated patient datasets, including single-cell and spatial transcriptomics, multiplex imaging, digital pathology, radiology imaging, and longitudinal clinical data. Funded projects span lung, head and neck, melanoma, and liver cancers and focus on discovering tumor-intrinsic and microenvironmental mechanisms of immunotherapy resistance.

A central goal of the position is to use computational analysis not only to develop or apply methods, but to make new, biologically grounded discoveries. The candidate will formulate scientific questions, identify and apply appropriate analytical approaches, integrate complementary data modalities, and generate mechanistic hypotheses that can be tested with experimental and clinical scientists using patient-derived organoids, cellular systems, and in vivo models established in the laboratory.

The position offers a highly interdisciplinary training environment at the interface of computational biology, translational cancer research, and clinical investigation. The candidate will work closely with computational scientists, cancer biologists, physician-scientists, pathologists, radiologists, and bioengineers, with opportunities to lead first/co-first author publications, develop independent research directions, and pursue fellowship and career development funding.

Qualifications:
• Doctoral degree in computational biology, bioinformatics, biostatistics, computer science, biomedical engineering, quantitative biology, genomics, or a related field.
• Strong programming and quantitative analysis skills and experience analyzing complex biological or biomedical datasets. Prior experience working with human or patient-derived data is preferred but not required.
• Experience in one or more areas such as single-cell or spatial omics, multiplex imaging, cancer genomics, immuno-oncology, machine learning, or computer vision is desirable. Candidates are not expected to have expertise across all data modalities.
• Self-driven with problem-solving skills who can identify important biological questions, learn and apply appropriate analytical approaches, and interpret computational findings in a biological and translational context.
• Strong communication skills, scientific curiosity, and the ability to work collaboratively with computational, experimental, and clinical researchers are essential. Prior publications demonstrating independent computational or quantitative research are preferred but not required.

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