2027 Summer Intern - Translational Safety (Computational Toxicology)

The Opportunity

The Computational Toxicology group is seeking a motivated summer intern to develop and apply computational approaches to biological questions in target safety research. The project will use single-cell transcriptomic and multi-omics data, alongside AI/ML methods, to investigate biological systems relevant to preclinical safety assessment.

Working with an interdisciplinary team, the intern will have opportunities to:

• Develop reproducible computational workflows to integrate and analyze single-cell transcriptomic and multi-omics datasets.
• Characterize in vitro models and assess their biological relevance through quantitative analyses and comparisons with reference datasets.
• Explore and benchmark AI/ML methods, including foundation models, for analyzing high-dimensional biological data.
• Collaborate with computational and experimental scientists to interpret findings and contribute to reusable analysis tools, including LLM-based or agentic workflows where appropriate.

Who You Are (Required)

Required Education: You meet one of the following criteria: Must be pursuing a Master's Degree (enrolled student). Must have attained a Master's Degree. Must be pursuing a PhD (enrolled student).

Required Majors: Bioinformatics, Computational Biology, Bioengineering, Computer Science, Data Science, Statistics, Machine Learning & AI, or related fields.

Required Skills

• Coding: Experience with Python or R for scientific data analysis and computational workflows.
• Bioinformatics: Familiarity with biological datasets (e.g., gene expression or other omics data) and associated sample and experimental metadata.
• Data Analysis: Ability to explore, quality-check, analyze, and interpret complex biological datasets.
• Research Practices: Ability to document methods and maintain reproducible analyses using version control tools.
• Collaboration: Strong problem-solving, communication, and teamwork skills.

Preferred Knowledge, Skills, and Qualifications

• Familiarity with single-cell RNA-seq concepts and analysis tools (e.g., Scanpy/AnnData, Seurat, or scVI).
• Experience with differential expression, pathway analysis, or comparative transcriptomics across biological models.
• Interest in machine learning, representation learning, or foundation-model applications to biological data analysis.
• Familiarity with LLM-based tools and agentic workflows for scientific research.