Computational Biologist 1

A Computational Biologist 1 (CB1) position is available to support ongoing work in the lab of Dr. Megan Burger in the Department of Cell, Developmental and Cancer Biology. The Burger Lab investigates mechanisms regulating immune suppression in lung cancer to inform the design of next-generation immunotherapies for cancer patients. The CB1 will contribute to a project investigating tumor-intrinsic mechanisms of immune evasion in early lung cancer that could inform new therapeutic targets and biomarkers of immunotherapy response.

Under supervision, the CB1 will function as the lab’s primary computational representative in an interdisciplinary collaboration on this project, carrying out integrative analyses of multi-omic data and assisting in biological interpretation that informs wet lab validation studies. The candidate will design and maintain software pipelines for analysis of multivariate datasets and develop and apply tools for dataset integration. Support will also be required for analysis of tumor imaging datasets for the project, including bioluminescence imaging, histology, and multiplexed imaging.

The ideal candidate will be a highly communicative team player able to coordinate with the team in goal setting, milestone completion and generation of publication-ready figures to support manuscripts, grants and publications.

Data Organization and Management

• Organize and maintain project metadata, raw data, processed data, analysis outputs, and figure files in a structured and accessible format.
• Maintain reproducible analysis workflows using clear code documentation, version control, shared repositories (e.g., GitHub), and standardized project documentation.
• Prepare datasets, code, workflows, and documentation for manuscript submission and public data deposition (e.g., GEO).

Data Analysis

• Perform quality control and preprocessing of bulk and single-cell RNA-sequencing and proteomic datasets for the assigned lung cancer immune evasion project, including assessment of sample quality, batch effects, and normalization.
• Analyze bulk transcriptomic and proteomic datasets using appropriate statistical and bioinformatic approaches, including differential abundance/expression analysis and pathway enrichment.
• Analyze single-cell RNA-seq datasets using existing workflows including dimensionality reduction, clustering, cell type annotation, differential expression, and trajectory and cell-state analysis.
• Develop and apply workflows to integrate proteomic and transcriptomic datasets and identify tumor-intrinsic pathways associated with immune evasion.
• Support biological interpretation of computational results by identifying candidate pathways, biomarkers, or therapeutic targets for wet lab validation.
• Support analysis of tumor imaging datasets for the project, including bioluminescence imaging, histology, and multiplexed imaging.
• Generate clear, interpretable visualizations of high-dimensional datasets, including heatmaps, volcano plots, dimensionality-reduction plots, pathway/network diagrams, and integrated multi-omic summary figures.

Communication and Scientific Reporting

• Serve as the lab’s primary computational representative for the collaborative lung cancer immune evasion project under the PI’s supervision.
• Coordinate with wet-lab and computational collaborators to define analysis goals, interpret findings, prioritize validation experiments, track milestones, and support timely completion of project deliverables.
• Present analysis plans, progress updates, results, and biological interpretations at regular lab and collaborative team meetings.
• Contribute to written computational methods and publication-ready figures to project manuscripts, grants, and related presentations and progress reports.

General Lab Support

• Assist with computational troubleshooting, data interpretation, and project coordination related to the lung cancer immune evasion project.
• Perform additional duties as assigned by the PI that are consistent with the goals of the project and position.

Career Development

• Participate in training, workshops, seminars, or other activities that enhance relevant computational biology skills.
• Stay current with emerging tools and best practices in multi-omic data analysis and integration.

Other duties as assigned.

• Master's Degree in Computational Biology or related field OR

• Bachelor's Degree in Computational Biology or related field AND

  3 years of relevant experience.

Knowledge, Skills, andAbilities

• Must have demonstrated experience with multi-omic data integration of proteomic and single-cell RNA-sequencing datasets.
• Demonstrated proficiency with proteomic data analysis and bulk and single-cell RNA-seq workflows, including quality control, normalization, differential expression analysis, dimensionality reduction, clustering, or cell-state analysis.
• Sustained experience working and communicating effectively within an interdisciplinary collaborative team with wet-lab scientists and computational biologists.
• Demonstrated experience generating clear data visualizations and publication-quality figures for manuscripts, grants, and presentations.
• Proficiency in R, Python, and/or Bash, with experience using relevant tools such as RStudio, Seurat, DESeq2, FastQC.
• Demonstrated skills in biological pathways analysis and network visualization (GSEA, IPA, Cytoscape) and interpretation of omic datasets in the context of tumor-immune interactions.
• Demonstrated ability to integrate proteomic and transcriptomic datasets to identify candidate biomarkers or therapeutic targets relevant to tumor immunology.
• Track record of effective collaboration with wet-lab scientists to prioritize candidates for experimental validation.
• Track record of maintaining organized, reproducible, and well-documented analysis workflows.
• Demonstrated ability to communicate computational methods and results through writing and publication-ready figures for manuscripts, grants, and presentations.

• Master’s Degree in Computational Biology AND 2 years of relevant experience.
• Knowledge of tumor imaging analysis and tools such as QuPath, Living Image, and MCMICRO.