Postdoctoral Research Fellows in Medical AI

Overview

Prof. Marinka Zitnik invites applications for a Postdoctoral Research Fellowship position at Harvard University.

This is an exceptional opportunity to advance the field of medical AI through cutting-edge research in generative AI and its transformative applications in precision medicine and health systems.

The successful candidate will lead research projects that aim to shape the future of medical AI. Potential research directions include:

  • Patient-centric generative AI to advance precision medicine and deliver tailored treatments for diverse patient populations.
  • Dynamic representations of evolving medical knowledge to bridge the gap between scientific discoveries and rich patient datasets.
  • Integration of large language models and medical knowledge graphs to unify biomedical data, reduce uncertainty, and enable actionable predictions.
  • Multimodal AI for global health decision-making to develop reliable, agentic AI systems and knowledge graph-powered LLMs for clinical support, rooted in global, country-specific, and context-appropriate medical guidelines.

This position offers unique opportunities for collaboration with global research foundations, patient-led organizations, and international health systems, fostering a truly interdisciplinary and impactful research environment.

Interested candidates are encouraged to explore our recent publications and research directions before submitting their applications.

Qualifications

We are seeking highly motivated applicants with expertise in one or more of the following areas: agentic AI, large language models, large-scale knowledge graphs, medical foundation models, multimodal learning, and generative AI. Strong technical skills and prior experience in precision medicine are essential.

Ideal candidates will have demonstrably strong research skills, evidenced by multiple publications in top-tier machine learning or artificial intelligence conferences and/or leading medical journals.

Candidates must hold a Ph.D. or equivalent degree in machine learning, computer science or a closely related field.

This position involves close collaboration with global research foundations, patient-led organizations, and international health systems. Strong communication skills and experience working in interdisciplinary teams are highly desirable.

Excellent programming skills and practical experience with leading machine learning frameworks are required. Applicants must have experience applying AI to medicine. Familiarity with modern AI environments, including multi-GPU model training and large-scale inference on dozens to hundreds of GPUs, is a significant advantage.

Application process

The position is available immediately and can be renewed annually. Interested applicants should submit the following documents via email to Prof. Zitnik and use the subject line “Postdoctoral Research Fellows in Medical AI”:

  • Curriculum Vitae (include links to your academic webpage and GitHub repositories for methods you developed)
  • Three representative publications (preprints are acceptable)
  • Statement of research (two pages) describing prior research experience and future research plan
  • Contacts for three letters of recommendation (the letters will be solicited after the initial review)

We are currently reviewing applications. Interested candidates are encouraged to submit their applications early.

Advisor

Marinka Zitnik is an Assistant Professor at Harvard University with appointments in the Department of Biomedical Informatics, Kempner Institute for the Study of Natural and Artificial Intelligence, Broad Institute of MIT and Harvard, and Harvard Data Science. We investigate machine learning with a current focus on learning systems informed by geometry, structure, and symmetry and grounded in knowledge. This approach creates foundational models, including pre-trained, self-supervised, multi-purpose, and multi-modal models trained at scale to enable broad generalization. Our methods produce actionable outputs to advance biological problems past the state of the art and open up new opportunities.

Dr. Zitnik has published extensively in top ML venues, such as NeurIPS, ICLR, ICML, and leading scientific journals, including Nature, Nature Methods, Nature Communications, and PNAS. She has organized numerous workshops and tutorials in the nexus of AI, deep learning, AI4Science and AI4Medicine at leading conferences, where she is also in the organizing committees.

Her research received best paper and research awards from International Society for Computational Biology, International Conference on Machine Learning, Bayer Early Excellence in Science Award, Amazon Faculty Research Award, Google Faculty Research Scholar Award, Roche Alliance with Distinguished Scientists Award, Sanofi iDEA-iTECH Award, Rising Star Award in Electrical Engineering and Computer Science (EECS), and Next Generation Recognition in Biomedicine, being the only young scientist with such recognition in both EECS and Biomedicine. Dr. Zitnik received the Kavli Fellowship by the US National Academy of Sciences and the Kaneb Fellowship award at Harvard Medical School. She also received the NSF CAREER Award.

Dr. Zitnik is an ELLIS Scholar in the European Laboratory for Learning and Intelligent Systems (ELLIS) Society. She is a member of the Science Working Group at NASA Space Biology. Dr. Zitnik co-founded Therapeutics Data Commons and is the faculty lead of the AI4Science initiative. Dr. Zitnik is the recipient of the 2022 Young Mentor Award at Harvard Medical School.


Harvard is an Equal Opportunity Employer.

Latest News

Jan 2025:   LLM and KG+LLM agent papers at ICLR

Jan 2025:   Artificial Intelligence in Medicine 2

Excited to share our new graduate course on Artificial Intelligence in Medicine 2.

Jan 2025:   ProCyon AI Highlighted by Kempner

Thanks to Kempner Institute for highlighting our latest research, ProCyon, our protein-text foundation model for modeling protein functions.

Jan 2025:   AI Design of Proteins for Therapeutics

Dec 2024:   Unified Clinical Vocabulary Embeddings

New paper: A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies. (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.

Dec 2024:   SPECTRA in Nature Machine Intelligence

Are biomedical AI models truly as smart as they seem? SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity. SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.

Nov 2024:   Ayush Noori Selected as a Rhodes Scholar

Congratulations to Ayush Noori on being named a Rhodes Scholar! Such an incredible achievement!

Nov 2024:   PocketGen in Nature Machine Intelligence

Oct 2024:   Activity Cliffs in Molecular Properties

Oct 2024:   Knowledge Graph Agent for Medical Reasoning

Sep 2024:   Three Papers Accepted to NeurIPS

Exciting projects include a unified multi-task time series model, a flow-matching approach for generating protein pockets using geometric priors, and a tokenization method that produces invariant molecular representations for integration into large language models.

Sep 2024:   TxGNN Published in Nature Medicine

Aug 2024:   Graph AI in Medicine

Excited to share a new perspective on Graph Artificial Intelligence in Medicine in Annual Reviews.

Aug 2024:   How Proteins Behave in Context

Harvard Medicine News on our new AI tool that captures how proteins behave in context. Kempner Institute on how context matters for foundation models in biology.

Jul 2024:   PINNACLE in Nature Methods

PINNACLE contextual AI model is published in Nature Methods. Paper. Research Briefing. Project website.

Jul 2024:   Digital Twins as Global Health and Disease Models of Individuals

Paper on digitial twins outlining strategies to leverage molecular and computational techniques to construct dynamic digital twins on the scale of populations to individuals.

Jul 2024:   Three Papers: TrialBench, 3D Structure Design, LLM Editing

Zitnik Lab  ·  Artificial Intelligence in Medicine and Science  ·  Harvard  ·  Department of Biomedical Informatics