Summer 2026 - Informatics and Predictive Sciences Internship

Role Summary

Summer internship dedicated to piloting innovative machine learning workflows to support cross-study integration of clinical trial data. In this role, you will collaborate across informatics and pharmacometrics teams to enhance a variational autoencoder (VAE) pipeline for translating patient-level data between unpaired and heterogeneous datasets—an important advancement for precision medicine and translational research. The internship will take place June–August 2026.

Responsibilities

• Conduct literature reviews and benchmark current state-of-the-art cross-modality data translation methods
• Implement and customize generative models tailored to clinical trial datasets with minimal feature overlap
• Evaluate translation performance using cluster robustness, correlation, and other relevant metrics
• Explore and prototype supervised extensions, including clinical outcome integration and uncertainty quantification
• Document code, create reproducible analysis scripts, and contribute to packaging methods for future deployment and cross-team collaboration

Qualifications

• Preferred: Enrolled in a PhD program in bioinformatics, computational biology, or a quantitative discipline
• Demonstrated scientific curiosity and interest in translational research and drug development
• Strong programming skills in Python, including use of PyTorch and standard data science libraries
• Strong interest in gaining hands-on experience with deep learning models
• Experience in data wrangling, statistical analysis, and data visualization
• Ability to research, critically evaluate, and synthesize scientific literature
• Ability to work independently and as a member of a broader computational team
• All candidates must be authorized to work in the US both at the time of hire and for the duration of their employment. Immigration or visa sponsorship is not available for this position.