Open Positions

Thank you for being so interested in joining our group! Impactful research requires quality mentoring relationships. Prof. Zitnik is the recipient of the 2022 Young Mentor Award at Harvard Medical School—this most prestigious award acknowledges that recognition to her.

Graduate students

We are taking on new PhD students each year.

If you are a current or a newly admitted PhD student interested in AI, machine learning and/or applications in genomics, therapeutics, medicine, and health, 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, programs within Harvard Integrated Life Sciences, and other programs at Harvard. We also recruit graduate students from Health Sciences & Technology at Harvard and MIT and other programs at MIT.

Postdoctoral research fellows in foundational AI

We have openings for postdoctoral research fellows in fundamental methods in the broad area of geometric deep learning, transfer learning, large-scale knowledge graphs, large language models, and generative AI.

Applications are reviewed on a rolling basis. Interested candidates are encouraged to submit their applications early.

NOW OPEN: Request For Applications

Postdoctoral research fellows in AI for cancer drug discovery

We have an opening for a postdoctoral research fellow in novel machine learning approaches to cancer drug discovery.

CLOSED: Request For Applications

Postdoctoral research fellows with Harvard Data Science Initiative

The Harvard Data Science Initiative (HDSI) postdoctoral fellows are outstanding early-career researchers. HDSI fellows work independently over a two to three-year fellowship with the guidance and partnership of Harvard University faculty.

CLOSED: Request For Applications

Harvard/MIT undergraduates, Masters’s students, Harvard affiliates

We are looking for outstanding Harvard undergraduates, Masters’s students, and other Harvard affiliates on a rolling basis. While we take students at all levels, excellent grades and/or prior experience in machine learning/AI is a plus. Generally, we expect:

  • Students commit 20 hours per week to research (ideally more).
  • Students commit to at least 6 months of research with the lab (ideally more).
  • Prior experience in AI and machine learning may include online courses. We encourage students to self-study relevant coursework. In addition, we provide mentoring on cutting-edge research.

Email Prof. Zitnik. Include your CV, current academic status, a summary of research experience, and brief highlights of projects.

Visitors, interns, and short-term students

We generally prefer visitors to stay for at least 6 months 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. Do not take this personally! We do review all applications!

Harvard is an Equal Opportunity Employer.

Latest News

May 2023:   Congratulations to Ada and Michelle

Congrats to PhD student Michelle on being selected as the 2023 Albert J. Ryan Fellow and also to participate in the Heidelberg Laureate Forum. Congratulations to PhD student Ada for being selected as the Kempner Institute Graduate Fellow!

Apr 2023:   Universal Domain Adaptation at ICML 2023

New paper introducing the first model for closed-set and universal domain adaptation on time series accepted at ICML 2023. Raincoat addresses feature and label shifts and can detect private labels. Project website.

Apr 2023:   Celebrating Achievements of Our Undergrads

Undergraduate researchers Ziyuan, Nick, Yepeng, Jiali, Julia, and Marissa are moving onto their PhD research in Computer Science, Systems Biology, Neuroscience, and Biological & Medical Sciences at Harvard, MIT, Carnegie Mellon University, and UMass Lowell. We are excited for the bright future they created for themselves.

Apr 2023:   Welcoming a New Postdoctoral Fellow

An enthusiastic welcome to Tianlong Chen, our newly appointed postdoctoral fellow.

Apr 2023:   New Study in Nature Machine Intelligence

New paper in Nature Machine Intelligence introducing the blueprint for multimodal learning with graphs.

Mar 2023:   Precision Health in Nature Machine Intelligence

New paper with NASA in Nature Machine Intelligence on biomonitoring and precision health in deep space supported by artificial intelligence.

Mar 2023:   Self-Driving Labs in Nature Machine Intelligence

Mar 2023:   TxGNN - Zero-shot prediction of therapeutic use

Mar 2023:   GraphXAI published in Scientific Data

Feb 2023:   Welcoming New Postdoctoral Fellows

A warm welcome to postdoctoral fellows Wanxiang Shen and Ruth Johnson. Congratulations to Ruthie for being named a Berkowitz Fellow.

Feb 2023:   New Preprint on Distribution Shifts

Feb 2023:   PrimeKG published in Scientific Data

Jan 2023:   GNNDelete published at ICLR 2023

Jan 2023:   New Network Principle for Molecular Phenotypes

Dec 2022:   Can we shorten rare disease diagnostic odyssey?

New preprint! Geometric deep learning for diagnosing patients with rare genetic diseases. Implications for using deep learning on sparsely-labeled medical datasets. Thankful for this collaboration with Zak Lab. Project website.

Nov 2022:   Can AI transform the way we discover new drugs?

Our conversation with Harvard Medicine News highlights recent developments and new features in Therapeutics Data Commons.

Oct 2022:   New Paper in Nature Biomedical Engineering

New paper on graph representation learning in biomedicine and healthcare published in Nature Biomedical Engineering.

Sep 2022:   New Paper in Nature Chemical Biology

Our paper on artificial intelligence foundation for therapeutic science is published in Nature Chemical Biology.

Sep 2022:   Self-Supervised Pre-Training at NeurIPS 2022

New paper on self-supervised contrastive pre-training accepted at NeurIPS 2022. Project page. Thankful for this collaboration with the Lincoln National Laboratory.

Sep 2022:   Best Paper Honorable Mention Award at IEEE VIS

Our paper on user-centric AI of drug repurposing received the Best Paper Honorable Mention Award at IEEE VIS 2022. Thankful for this collaboration with Gehlenborg Lab.

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