The Systems Biology PhD Training Program starts from a clear need. Modern biomedical research depends on scientists who can work across experimental and computational modes with equal confidence. Building virtual cells, mapping tissue architecture at scale, developing digital models that can replace animal studies, and designing new measurement technologies all require this combination of skills. Our program is built to train people who can do that work.

Our approach to training cuts across traditional boundaries. Students learn to move easily between the bench and the computer, treating engineering, machine learning, and applied mathematics as standard parts of their scientific practice. They gain hands-on experience with pipetting, sample preparation, imaging, and other core experimental techniques, along with data analysis, modeling, and computational tool building. The goal is to integrate these abilities rather than keep them in separate silos.

We emphasize this breadth for a reason. Researchers who are comfortable with a wide range of tools are better positioned to explore unconventional questions and develop new approaches. By exposing students to multiple ways of thinking and working, we aim to give them the confidence to pursue ideas that open new directions and contribute meaningfully to the future of biomedical science.