Open Research Positions

Thank you for your interest in joining our lab!

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.

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.

NOW OPEN: 2021 Request For Applications

Postdoctoral research fellow in ML for COVID-19

We have a new opening for a postdoctoral research fellowship position in novel machine learning methods to combat COVID-19!

We are currently reviewing applications for this position. Interested candidates are encouraged to submit their applications as soon as possible and preferably by September 1st, 2020. We will continue accepting applications after this deadline if the position is not filled.

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

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

We are excited to see published our paper on DeepPurpose, a deep learning library for drug-target interaction prediction, and a paper on skipGNN, a graph neural network for predicting molecular interactions.

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

Hot off the press! MARS, an approach for discovering novel cell types across heterogeneous single-cell experiments is just published in Nature Methods.

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.

Jul 2020:   Postdoctoral Research Fellowship

We have a new opening for a postdoctoral research fellow in novel machine learning methods to combat COVID-19! Submit your application by September 1, 2020.

Jul 2020:   DeepPurpose Library

DeepPurpose is a deep learning library for drug-target interaction prediction and applications to drug repurposing and virtual screening.

Jun 2020:   Subgraph Neural Networks

Subgraph neural networks learn powerful subgraph representations that create fundamentally new opportunities for predictions beyond nodes, edges, and entire graphs.

Jun 2020:   Defense Against Adversarial Attacks

GNNGuard can defend graph neural networks against a variety of training-time attacks. Remarkably, GNNGuard can restore state-of-the-art performance of any GNN in the face of adversarial attacks.

Jun 2020:   Graph Meta Learning via Subgraphs

G-Meta is a meta-learning approach for graphs that quickly adapts to new prediction tasks using only a handful of data points. G-Meta works in most challenging, few-shot learning settings and scales to massive interactomics data as we show on our new Networks of Life dataset comprising of 1,840 networks.

May 2020:   The Open Graph Benchmark

A new paper introducing the Open Graph Benchmark, a diverse set of challenging and realistic benchmark datasets for graph machine learning.

May 2020:   Special Issue on AI for COVID-19

Marinka is co-editing a special issue of IEEE Big Data on AI for COVID-19. In light of the urgent need for data-driven solutions to mitigate the COVID-19 pandemic, the special issue will aim for a fast-track peer review.

May 2020:   Multiscale Interactome

A new preprint on discovering disease treatment mechanisms through the multiscale interactome.

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