The Eric and Wendy Schmidt Center brings together a wide range of researchers to identify key challenges; generate new kinds of data; create novel theoretical, computational, and algorithmic paradigms — and apply these insights to solve the most pressing problems of biology and medicine.
We fund postdoctoral associates with research interests in any area of machine learning, statistics, or applied mathematics that could drive the frontier of biomedical research. Postdocs choose a computational and biomedical mentor from Broad-, MIT-, or Harvard-affiliated faculty.
One-year appointment with the possibility of renewal for a second and third year based upon satisfactory performance
Experience in machine learning or related fields is critical for the role, while prior exposure to biology or medicine is helpful but optional
Apply now via the AJO portal.
The review period begins on December 20, 2024 with appointments starting on/after July 1, 2025 (fellowship start date is flexible).
The Schmidt Center was established to unite the disciplines of machine learning and biology to realize the promise that rests in unlocking a greater understanding of the most pressing questions in the life sciences. We seek to achieve our mission through three guiding pillars: (1) supporting research at the intersection of machine learning (ML) and biology; (2) developing a robust community that is globally based; and (3) training the next generation of leaders that will be fluent in both fields.
Embedded in a world-class research community at the Broad Institute
Mentorship from computational and biomedical researchers across the Broad, MIT, and Harvard
Become part of a highly collaborative and active center with regular talks, group meetings, coffee socials, and other events at the nexus of machine learning and biology
Access to high-end computing resources
Schmidt Center fellows are advancing machine learning’s potential to drive biomedical discoveries. Here’s what they're working on.
By sidestepping the need for costly interventions, a new method could potentially reveal gene regulatory programs, paving the way for targeted treatments.
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Yue Qin talks about the unique advantages of interdisciplinary science that bridges biology and computer science.
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