Where ML Meets Biomedicine—Innovate This Summer

• Join our Computational Biology Principal Scientist to push the boundaries of drug discovery by leveraging advanced graph neural networks to integrate multi-modal genomics data with protein-protein interaction networks—transforming AI into real-world biomedical breakthroughs.
• Our successful candidate will embark on a dynamic 12-week summer internship, tackling impactful projects at the intersection of AI and drug discovery. You’ll implement cutting-edge graph-based machine learning models, develop integration pipelines for multi-modal omics datasets, and build robust validation frameworks for druggability predictions. You’ll contribute to documentation and reproducible analysis workflows, ensuring your research leaves a lasting impact. The internship culminates in a final presentation and report, giving you the opportunity to showcase your work and shape the future of AI-driven biomedicine.

You will

• Develop and implement graph neural network architectures to capture protein-protein interaction networks
• Integrate and analyze GWAS, functional genomics data with existing druggability features
• Validate predictions using Open Targets data
• Document methodology and results
• Present findings to the research team

You are

• Currently enrolled in a graduate program in Computational Biology, Bioinformatics, Computer Science, or related field
• A strong Python programmer
• Experienced with machine learning frameworks (PyTorch, TensorFlow, or similar)
• Knowledgable of biological networks and genomics data analysis
• Strong analytical and problem-solving skills

You will stand out if you have

• Experience with graph neural networks or graph embedding techniques
• Familiarity with drug discovery concepts and terminology
• Previous work with biological network analysis
• Experience with large-scale genomics data processing
• Knowledge of drug target identification methods