Summer 2026 - Computational Biology Internship

Role Summary

Identifying causal drivers of cancer progression is essential for developing effective anti-cancer therapies. To achieve this, we integrate multi-modal real-world patient data (RWD) to identify key drivers that regulate key oncogenic processes and transcriptional programs. These drivers represent an opportunity for therapeutic intervention due to their disease-relevant regulatory roles. However, many existing approaches to identify these drivers rely solely on expression relationships between gene pairs, ignoring somatic alterations that are common in cancers and often independently affect regulator activity that is not captured in expression data. The full-time internship will take place June - August 2026.

Responsibilities

• Aid in the development of a novel statistical model that integrates transcriptomic and somatic alteration data to infer regulons for diver genes
• Benchmark the performance of this method in patient and compound perturbation data by evaluating key biological pathways
• Cross-validate inference results across different cohorts and suggest improvement
• Deliver and communicate analysis results to internal teams

Qualifications

• Currently enrolled in a PhD program in bioinformatics, computer science, biostatistics, data science, or a similar quantitative field
• A strong foundation in statistics and machine learning methods
• Experienced in transcriptomic analyses
• Highly skilled in one or more scripting languages (ex. R or python)
• Excellent communication and presentation skills
• All candidates must be authorized to work in the US both at the time of hire and for the duration of their employment. Please note there immigration or visa sponsorship is not available for this position.