Postdoctoral Research Fellow in AI for Cancer Drug Discovery

Overview

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.

Qualifications

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 Employer.

Latest News

Nov 2024:   Ayush Noori Selected as a Rhodes Scholar

Congratulations to Ayush Noori on being named a Rhodes Scholar! Such an incredible achievement!

Nov 2024:   PocketGen in Nature Machine Intelligence

Oct 2024:   Activity Cliffs in Molecular Property Prediction

Oct 2024:   Knowledge Graph Agent for Medical Reasoning

Sep 2024:   Three Papers Accepted to NeurIPS

Exciting projects include a unified multi-task time series model, a flow-matching approach for generating protein pockets using geometric priors, and a tokenization method that produces invariant molecular representations for integration into large language models.

Sep 2024:   TxGNN Published in Nature Medicine

Aug 2024:   Graph AI in Medicine

Excited to share a new perspective on Graph Artificial Intelligence in Medicine in Annual Reviews.

Aug 2024:   How Proteins Behave in Context

Harvard Medicine News on our new AI tool that captures how proteins behave in context. Kempner Institute on how context matters for foundation models in biology.

Jul 2024:   PINNACLE in Nature Methods

PINNACLE contextual AI model is published in Nature Methods. Paper. Research Briefing. Project website.

Jul 2024:   Digital Twins as Global Health and Disease Models of Individuals

Paper on digitial twins outlining strategies to leverage molecular and computational techniques to construct dynamic digital twins on the scale of populations to individuals.

Jul 2024:   Three Papers: TrialBench, 3D Structure Design, LLM Editing

Jun 2024:   TDC-2: Multimodal Foundation for Therapeutics

The Commons 2.0 (TDC-2) is an overhaul of Therapeutic Data Commons to catalyze research in multimodal models for drug discovery by unifying single-cell biology of diseases, biochemistry of molecules, and effects of drugs through multimodal datasets, AI-powered API endpoints, new tasks and benchmarks. Our paper.

May 2024:   Broad MIA: Protein Language Models

Apr 2024:   Biomedical AI Agents

Mar 2024:   Efficient ML Seminar Series

We started a Harvard University Efficient ML Seminar Series. Congrats to Jonathan for spearheading this initiative. Harvard Magazine covered the first meeting focusing on LLMs.

Mar 2024:   UniTS - Unified Time Series Model

UniTS is a unified time series model that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. Project website.

Mar 2024:   Weintraub Graduate Student Award

Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. News Story. Congratulations!

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