Thank you for your interest in joining our research group!
We are taking on new PhD students each year.
If you are a current or a newly admitted PhD student excited about machine learning and/or applications in genomics, medicine, and health, please email Prof. Zitnik directly. Include your CV and a brief description of your research interests.
We are recruiting PhD students from a number of graduate programs, including Bioinformatics and Integrative Genomics, Systems Biology, Biological and Biomedical Sciences, Harvard Integrated Life Sciences, and other programs at Harvard. We also recruit graduate students from Health Sciences & Technology programs at Harvard and MIT.
Postdoctoral research fellows in knowledge graphs and graph ML
We have an opening for a postdoctoral research fellowship in novel methods for knowledge graphs and graph representation learning.
This position in available immediately. Interested candidates are encouraged to submit their applications as soon as possible.
Postdoctoral fellows in the Eric and Wendy Schmidt Center (EWSC) at the Broad Institute of MIT and Harvard
The Eric and Wendy Schmidt Center (EWSC) at the Broad Institute of MIT and Harvard is seeking exceptional postdoctoral fellows to join the newly-launched center. The EWSC seeks to understand the programs of life and how they connect across biological scales–from the genetic to the cellular to the organismal–by creating a strong community at the interface of machine learning (ML) and biology.
In the cover letter, include potential avenues of collaboration and supervision by Prof. Zitnik.
The Harvard Data Science postdoctoral fellows
The Harvard Data Science Initiative (HDSI) postdoctoral fellows are outstanding early-career researchers whose interests lie in a number of different fields. HDSI fellows work independently over a two to three year fellowship with the guidance and partnership of Harvard University faculty.
Postdoctoral research fellows in ML/AI
We are looking for Postdoctoral Fellows for research in novel machine learning and data science methods. The successful candidate will lead research in machine learning methods as well as applications of the methods to some of the most impactful datasets.
Biomedical data involve rich interactions that span from the molecular level to the level of connections between diseases in a patient and to the societal level encompassing all human interactions. These interactions at different levels give rise to a bewildering degree of complexity, which is only likely to be fully understood through data-driven and computationally enabled study. This scientific approach not only opens up new avenues for understanding nature, analyzing health, and developing medicines to help people but can impact on the way predictive modeling is performed today at the fundamental level.
Among others, possible research projects include:
- Machine learning for biomedical data in efforts to set sights on new frontiers in genomics, drug discovery, and precision health beyond classic neural networks on image and sequence data.
- Representation learning, embedding methods, and graph neural networks in efforts to bridge the divide between research data and patient data.
- Fusion, learning and reasoning for knowledge graphs in efforts to combine biomedical data in their broadest sense, reduce redundancy and uncertainty, and make actionable predictions.
- Next-generation algorithms for networks, focusing on large networks of interactions between biomedical entities and their applications to network biology and medicine.
- Contextually adaptive AI in efforts to advance algorithms to train more with less data and reason about never-before-seen phenomena as algorithms encounter new patients, diseases, or cell types.
We seek highly-motivated candidate with strong research skills and background in machine learning and/or applications on biomedical data. Candidates must have a Ph.D. or equivalent degree in computer science, statistics, engineering, biomedical informatics, computational biology or a closely related field.
Strong programming skills and experience with large-scale data and machine learning frameworks are required.
How to apply
Submit your application with a letter indicating your interests and experience, a CV, names and email addresses of 2-3 references, and 2 of your best publications via email to Prof. Zitnik. Use the subject line “Postdoctoral Fellowship Application” in your email.
We highly encourage applicants to include links to any software they have developed. The position is available immediately and can be renewed annually.
Harvard undergraduate & Masters students
On a rolling basis, we are looking for Harvard undergraduate and Masters students. While we take undergraduate and Masters students at all levels, excellent grades and/or prior experience in machine learning/AI is a plus. Generally, we expect:
- Students to commit to at least 10 hours per week to research.
- Researchers to commit to at least 6 months of research with the lab (ideally more).
- Researchers to have some prior experience in AI/ML and data science, which may include online courses. We encourage students to self-study relevant coursework and provide mentoring on the very recent advances in the research field.
If you are a current or admitted Harvard undergraduate or Masters student, please email Prof. Zitnik. Include your CV, current academic status, and any past research experience and non-course AI/ML-related projects.
Visitors, interns, and short-term students
We generally prefer visitors to stay for at least 6 months in order to carry out a high-quality research project.
Because of the large email load that Prof. Zitnik receives, she may not respond to all applicants. Please do not take this personally! We do review all applications!
Harvard is an Equal Opportunity/Affirmative Action Employer. Women and minorities are especially encouraged to apply.