Open Research Positions

Thank you for your interest in joining our research group!

Graduate students

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.

NOW OPEN: Request For Applications


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.

NOW OPEN: Request For Applications

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.

CLOSED: Request For Applications


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.

Qualifications

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.

Latest News

Apr 2021:   Representation Learning for Biomedical Nets

In our survey on representation learning for biomedical networks we discuss how long-standing principles of network biology and medicine provide the conceptual grounding for representation learning, explain its successes, and inform future advances.

Mar 2021:   Receiving Amazon Research Award

We are excited about receiving Amazon Faculty Research Award on Actionable Graph Learning for Finding Cures for Emerging Diseases. Thank you to Amazon Science for supporting our research.

Mar 2021:   Michelle's Graduate Research Fellowship

Michelle M. Li won the NSF Graduate Research Fellowship Award. Congratulations!

Mar 2021:   Hot Off the Press: Multiscale Interactome

Hot off the press! We develop a multiscale interactome approach to explain disease treatments. The approach can predict drug-disease treatments, identify proteins and biological functions related to treatment, and identify genes that alter treatment’s efficacy and adverse reactions.

Mar 2021:   Graph Networks in Computational Biology

We are excited to share slides from our recent lecture on Graph Neural Networks in Computational Biology, which we gave at Stanford ML for Graphs course.

Mar 2021:   Fair and Stable Graph Representation Learning

We are thrilled to share the latest preprint on fair and stable graph representation learning.

Feb 2021:   New Preprint on Therapeutics Data Commons

Jan 2021:   An Algorithmic Approach to Patient Safety

The new algorithmic approach investigates population-scale patient safety data and reveals inequalities in adverse events before and during COVID-19 pandemic.

Jan 2021:   Workshop on AI in Health at the Web Conference

We are excited to co-organize Workshop on AI in Health: Transferring and Integrating Knowledge for Better Health at the Web (WWW) conference. The call for papers is open! We also announce the AI in Health Data Challenge.

Jan 2021:   Tutorial on ML for Drug Development

We will present a tutorial on ML/AI for drug discovery and development at IJCAI conference. See the tutorial website.

Dec 2020:   Two New Papers Published

Dec 2020:   Bayer Early Excellence in Science Award

Our research won the Bayer Early Excellence in Science Award. We are honored to have received this recognition!

Nov 2020:   Therapeutics Data Commons (TDC)

We are thrilled to announce Therapeutics Data Commons (TDC)! We invite you to join TDC. TDC is an open-source and community-driven effort.

Nov 2020:   National Symposium on the Future of Drugs

On behalf of the NSF, we are organizing the National Symposium on Drug Repurposing for Future Pandemics. We have a stellar lineup of invited speakers! Register at www.drugsymposium.org.

Oct 2020:   MARS: Novel Cell Types in Single-cell Datasets

Sep 2020:   Four Papers Accepted at NeurIPS

Thrilled that our lab has 4 papers accepted at NeurIPS 2020! Congratulations to fantastic students and collaborators, Michelle, Xiang, Kexin, Sam, and Emily.

Sep 2020:   MITxHarvard Women in AI Interview

The MITxHarvard Women in AI initiative talked with Marinka about AI, machine learning, and the role of new technologies in biomedical research.

Aug 2020:   Trustworthy AI for Healthcare

We are excited to be co-organizing a workshop at AAAI 2021 on Trustworthy AI for Healthcare! We have a stellar lineup of speakers. Details to follow soon!

Aug 2020:   Network Drugs for COVID-19

What are network drugs? Drugs for COVID-19 predicted by network medicine, our graph neural networks (GNNs), and our rank aggregation algorithms, followed by experimental confirmations. The full paper is finally out!

Jul 2020:   Podcast on ML for Drug Development

Tune in to the podcast with Marinka about machine learning to drug development. The discussion focuses on open research questions in the field, including how to limit the search space of high-throughput screens, design drugs entirely from scratch, and identify likely side-effects of combining drugs in novel ways.

Zitnik Lab  ·  Harvard  ·  Department of Biomedical Informatics