Explore Academic Positions at Harvard Fellow in AI/ML for Scientific Applications, Foundation Models, and AI Systems Bookmark this Posting Print Preview | Apply for this Job Position Details Title Fellow in AI/ML for Scientific Applications, Foundation Models, and AI Systems School Faculty of Arts and Sciences Department/Area Kempner Institute for the Study of Natural and Artificial Intelligence Position Description
The Kempner Institute at Harvard University seeks early-career researchers to help shape the future of AI as Kempner AI Fellows. We are looking for candidates with strong foundations in modern machine learning and the ambition to advance foundation models, agentic systems, and new AI approaches for high-impact scientific applications. We seek candidates with strong technical preparation in modern AI/ML, a
demonstrated record of research accomplishment, and an interest in contributing to ambitious research at the frontier of machine learning and science. Areas of particular interest include: foundation model training, evaluation, and adaptation agentic workflows, tool-using models, and AI systems bespoke scientific applications of AI/ML, including in the life sciences alternative architectures and systems-level
approaches to modern AI AI Fellows will work closely with one another and with Kempner faculty, researchers, and students on foundational machine learning and domain-informed scientific applications. The position is particularly well-suited to candidates eager to deepen their technical skills in foundation models and agentic systems while applying those methods to important scientific and technical
problems.Appointment Terms Fellows will conduct research under the direction of a Kempner Institute investigator. Fellows are appointed for a one-year term; reappointment may be possible for up to three consecutive years. Due to the importance of in-person mentoring, this position is based on campus, full-time, at Harvard University. Remote work for this position is not possible. Basic Qualifications Bachelor’s or
master’s degree in computer science, statistics, electrical engineering, applied mathematics, computational biology/neurobiology, or a related quantitative field required by the expected start date Strong technical background in modern AI/ML, including deep learning and hands-on experience with frameworks such as PyTorch or JAX Demonstrated research productivity, including publications in venues such as ICML, ICLR,
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