Bioinformatician I

Duke Careers•Durham, NC

About The Position

The Oliver Lab at Duke University is seeking a highly motivated Bioinformatician II to join a multidisciplinary research team focused on understanding the mechanisms that drive tumor lineage plasticity, heterogeneity, and drug resistance in lung cancer. Building on recent discoveries (Finlay et al, Cancer Cell, 2024 and Ireland et al., Nature, 2025), our work integrates advanced computational and experimental approaches to define how tumors evolve under therapeutic pressure. This position offers the opportunity to work at the interface of cancer biology, genomics, and data science, leveraging large-scale datasets from genetically engineered mouse models, organoid systems, and human tumor specimens. The successful candidate will play a central role in analyzing and integrating single-cell, spatial transcriptomic, and multi-omic datasets to uncover new biological insights and therapeutic vulnerabilities.

Requirements

PhD in Bioinformatics, Computational Biology, Genomics, Computer Science, or a related field (or MS with substantial relevant experience)
Strong programming skills (e.g., Python, R, or equivalent)
Experience analyzing high-dimensional genomic data, particularly RNA-seq or single-cell datasets
Demonstrated ability to work independently and flexibly manage multiple projects
Strong communication skills and ability to collaborate in a team-oriented environment

Nice To Haves

Experience with single-cell and/or spatial transcriptomics analysis
Familiarity with multi-omics data integration
Experience applying machine learning or statistical modeling to biological datasets
Experience working in cancer biology or related biomedical fields
Experience with high-performance computing environments and reproducible workflows

Responsibilities

Lead and contribute to the analysis of single-cell RNA-seq, spatial transcriptomics, and multi-omic datasets from mouse and human cancer models
Develop and implement computational pipelines for large-scale genomic data analysis and integration
Apply statistical and machine learning approaches to identify cell states, lineage relationships, and mechanisms of therapy resistance
Collaborate closely with experimental biologists to design analyses, interpret results, and guide hypothesis generation
Contribute to manuscript preparation, grant applications, and presentation of findings at scientific meetings
Mentor trainees and contribute to a collaborative and interdisciplinary research environment