Prof. Marinka Zitnik invites applications for a Postdoctoral Research Fellowship position at Harvard University.
Selected candidates will be expected to lead research in foundation models focusing on geometric deep learning, large-scale knowledge graphs, large language models, multimodal learning, generative AI, and/or AI agents. In addition, fellows will have opportunities to transition novel algorithms to applications in therapeutic science and precision medicine.
We seek highly-motivated applicants with background in one or more of the following areas: geometric deep learning, large-scale knowledge graphs, large language models, transfer learning, and generative AI. Successful applicants will be strong technically as well as have an inclination towards real-world problems.
We are looking for applicants with demonstrably strong research skills, ideally, with multiple publications in top venues in machine learning and artificial intelligence, and/or top-tier scientific journals.
Candidates must have a Ph.D. or equivalent degree in computer science, statistics, or a closely related field. Strong programming skills and practical experience with leading machine learning frameworks are required. Experience and/or interest in applications of AI to science, biology and medicine is a strong plus.
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 Fellowship in Foundation AI”:
- Curriculum Vitae (include links to your academic webpage and GitHub repositories for methods you developed)
- Two representative publications (preprints are acceptable)
- Statement of research (two pages) describing prior research experience and future research plans
- Three letters of recommendation (will be solicited after the initial review)
We are currently reviewing applications. Interested candidates are encouraged to submit their applications early.
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 was named Kavli Fellow 2023 by the National Academy of Sciences.
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.