HMS - Postdoctoral Fellow in Zitnik Lab

Overview

We invite applicants for a postdoctoral fellow position in the Zitnik lab at Harvard Medical School in Boston, Massachusetts. The lab focuses on Artificial Intelligence for Medicine and Science.

The selected candidate will lead research in generative, multimodal, and agentic AI, as well as foundation models, with focus on geometric deep learning, large‑scale knowledge graphs, and large language models. Fellows will also have the opportunity to apply these methods to address challenges in scientific discovery and precision medicine.

Applicants should have a strong background in agentic AI, geometric deep learning, large‑scale knowledge graphs, large language models, multimodal learning, or generative AI. They should be technically strong and have an inclination towards real‑world problems.

• Ph.D. or M.D./Ph.D. in machine learning, computer science, or a closely related field.
• Excellent programming skills and practical experience with leading machine learning frameworks and modern AI environments, including multi‑GPU model training and large‑scale inference on dozens to hundreds GPUs.
• Demonstrably strong research skills, evidenced by multiple publications in top‑tier machine learning or artificial intelligence conferences and/or leading scientific journals.
• Experience in applications of AI to biology and medicine is a strong plus.

This position is salaried and benefits eligible. Postdoctoral fellow salary, which is determined by the number of years post PhD, and benefits can be found at (Use the "Apply for this Job" box below)..

Harvard University is committed to equal opportunity and non‑discrimination. We seek talent from all parts of society and the world, and we strive to ensure everyone at Harvard thrives. Our differences help our community advance Harvard’s academic purposes. Harvard has an equal employment opportunity policy that outlines our commitment to prohibiting discrimination on the basis of race, ethnicity, color, national origin, sex, sexual orientation, gender identity, veteran status, religion, disability, or any other characteristic protected by law or identified in the university’s non‑discrimination policy.