Intern, Computational Oncology

Revolution Medicines • Redwood City, CA

1d • Hybrid

About The Position

Revolution Medicines is seeking a motivated summer intern to evaluate computational strategies for resolving tumor and tumor microenvironment (TME) states from bulk, single-cell, and spatial transcriptomic data in oncology. This project will focus on benchmarking deconvolution methods and assessing single-cell analysis best practices to determine the most biologically accurate and reproducible approaches for studying treatment response and resistance to targeted therapies, including RAS(ON) inhibitors.
Evaluate Deconvolution Methods:
Review leading bulk and spatial deconvolution tools.
Benchmark selected methods using curated datasets and single-cell references.
Assess robustness in detecting immune shifts, resistant tumor states, and TME remodeling.
Test Single-Cell Analysis Best Practices:
Compare normalization, integration, and batch correction strategies.
Evaluate clustering robustness and annotation reproducibility.
Assess the impact of different processing steps on biological interpretation.

Requirements

• Pursuing a BS or MS in Computational Biology, Bioinformatics, Systems Biology, or related field.
• Proficiency in R and Python.

Nice To Haves

• Experience analyzing RNA-seq data (bulk and/or single-cell).
• Experience with Seurat, Scanpy, or scVI.
• Familiarity with tumor microenvironment biology.

Responsibilities

• Review leading bulk and spatial deconvolution tools.
• Benchmark selected methods using curated datasets and single-cell references.
• Assess robustness in detecting immune shifts, resistant tumor states, and TME remodeling.
• Compare normalization, integration, and batch correction strategies.
• Evaluate clustering robustness and annotation reproducibility.
• Assess the impact of different processing steps on biological interpretation.

What This Job Offers

• Career Level: Intern