Postdoctoral Research Fellow in AI for Cancer Drug Discovery


Prof. Marinka Zitnik invites applications for a Postdoctoral Research Fellowship position at Harvard University. The fellow will lead the design, development, and implementation of novel AI methods for the analysis of clinical and biomarker data in oncology.

Immuno-oncology therapies are revolutionary in the treatment of cancer. However, there is much that is not understood about the drivers of efficacy, response, and resistance. The fellow will develop novel approaches in deep learning, graph AI, and transfer learning that will have multiple applications, including cancer biomarker discovery, target identification, selection of drug combination partners, patient enrichment, and disease characterization.

Harvard offers an exceedingly rich environment for education and career development, with opportunities for collaboration and learning not only within the Harvard Medical School but also at many other Harvard schools, institutes, and centers.

Additionally, the fellow will benefit from close collaboration and mentorship by Roche computational and biomarker scientists through the “Roche Access to Distinguished Scientists Programme”. Roche is the world’s largest biotech company and has over 20 immunotherapy molecules in development. This collaboration opens the door to applying newly developed methods to patient data from ongoing clinical trials. Our vision is to combine machine learning research with unique clinical data assets to generate novel insights ensuring the right treatment for the right patient at the right time.


We seek highly-motivated applicants with background in one or more of the following areas: deep learning, computational biology, therapeutic science, cancer data science, and network science. Successful applicants will be critical thinkers and problem solvers with the drive and demonstrated ability to translate ideas to action and complete projects effectively.

Applicants must possess a PhD degree or equivalent or be nearing the date of their thesis defense in computational biology, biomedical informatics, computer science, statistics or a closely related field. Strong programming skills and experience with deep learning frameworks and its biomedical applications are required.

We value diligence, ingenuity, initiative, collegiality, and integrity. We are looking for applicants with demonstrably strong research skills, ideally, with multiple publications in top venues in data science, machine learning and computational biology. Strong analytical and oral communication skills and an ability to work both independently and as a cooperative member of a team are required. While accomplished candidates with expertise in various aspects of biomedical AI will be considered, those with a background in cancer data science or therapeutic science are especially encouraged to apply. A competitive salary and excellent benefits package are available.

Application process

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 AI for Cancer Drug Discovery”:

  • Cover letter describing your interest in the position, relevant experience, and career goals
  • Curriculum Vitae (include link to your academic webpage and links to software you developed)
  • Two representative publications (e.g., published or unpublished manuscripts)
  • Names and email addresses of three references

We are currently reviewing applications for this position. Interested candidates are encouraged to submit their applications as soon as possible.

Faculty and mentors

Dr. Marinka Zitnik is an Assistant Professor at Harvard with appointments in the Department of Biomedical Informatics, Broad Institute of MIT and Harvard, and Harvard Data Science. Dr. Zitnik has published extensively in top ML venues and leading scientific journals. She has organized conferences and workshops in graph representation learning, drug discovery, and precision medicine at leading conferences (NeurIPS, ICLR, ICML, ISMB, AAAI, WWW), where she is also on the organizing committees. She is an ELLIS Scholar in the European Laboratory for Learning and Intelligent Systems (ELLIS) Society and a member of the Science Working Group at NASA Space Biology. Her research won paper and research awards from the International Society for Computational Biology, Bayer Early Excellence in Science, Amazon Faculty Research, Roche Alliance with Distinguished Scientists, Rising Star Award in Electrical Engineering and Computer Science, and Next Generation in Biomedicine Recognition, being the only young scientist with such recognition in both EECS and Biomedicine. She co-founded Therapeutics Data Commons and the AI for Science initiatives. Dr. Zitnik is the recipient of the 2022 Young Mentor Award at Harvard Medical School—this most prestigious award acknowledges her mentorship of the next generation of scientists who conduct research on artificial intelligence and biology.

Dr. Daniel Marbach, a computational biologist and Principal Scientist Bioinformatics at Roche, will serve as mentor for the fellow, together with his team of scientists. Dr. Marbach has long been engaged in open science collaborations involving academia and industry as an organizer of multiple DREAM Challenges, which included hundreds of participating teams and were published in leading journals. Dr. Marbach is an expert in computational network biology and advanced analytics in drug discovery.

Harvard is an Equal Opportunity/Affirmative Action Employer. Women and minorities are especially encouraged to apply.

Latest News

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.

Sep 2022:   Multimodal Representation Learning with Graphs

Aug 2022:   On Graph AI for Precision Medicine

The recording of our tutorial on using graph AI to advance precision medicine is available. Tune into four hours of interactive lectures about state-of-the-art graph AI methods and applications in precision medicine.

Aug 2022:   Evaluating Explainability for GNNs

New preprint! We introduce a resource for broad evaluation of the quality and reliability of GNN explanations, addressing challenges and providing solutions for GNN explainability. Project website.

Jul 2022:   New Frontiers in Graph Learning at NeurIPS

Excited to organize the New Frontiers in Graph Learning workshop at NeurIPS.

Jul 2022:   AI4Science at NeurIPS

We are excited to host the AI4Science meeting at NeurIPS discussing AI-driven scientific discovery, implementation and verification of AI in science, the influence AI has on the conduct of science, and more.

Jul 2022:   Graph AI for Precision Medicine at ISMB

Jul 2022:   Welcoming Fellows and Summer Students

Welcoming a research fellow Julia Balla and three Summer students, Nicholas Ho, Satvik Tripathi, and Isuru Herath.

Jun 2022:   Broadly Generalizable Pre-Training Approach

Excited to share a preprint on self-supervised method for pre-training. Project website with evaluation on eight datasets, including electrodiagnostic testing, human daily activity recognition, and health state monitoring.

Jun 2022:   Welcoming New Postdocs

Excited to welcome George Dasoulas and Huan He, new postdocs joining us this Summer.

May 2022:   George Named the 2022 Wojcicki Troper Fellow

May 2022:   New preprint on PrimeKG

New preprint on building knowledge graphs to enable precision medicine applications.

May 2022:   Building KGs to Support Precision Medicine

Apr 2022:   Webster on the Cover of Cell Systems

Webster is on the cover of April issue of Cell Systems. Webster uses cell viability changes following gene perturbation to automatically learn cellular functions and pathways from data.

Apr 2022:   NASA Space Biology

Dr. Zitnik will serve on the Science Working Group at NASA Space Biology.

Mar 2022:   Yasha's Graduate Research Fellowship

Yasha won the National Defense Science and Engineering Graduate (NDSEG) Fellowship. Congratulations!

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