Open Positions

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

Graduate students

We are taking on new PhD students every year.

If you are a current or a newly admitted PhD student interested in developing AI and machine learning methods and/or using AI to advance therapeutics and medicine, 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 Artificial Intelligence in Medicine, Bioinformatics and Integrative Genomics, Biomedical Informatics, 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/MIT and other programs such as EECS at MIT.

Postdoctoral research fellows and students in AI & therapeutics

We are building the next generation of Therapeutics Commons!

Zitnik Lab founded Therapeutics Commons, a global open-science initiative to access and evaluate AI across therapeutic modalities (small molecules, macro-molecules, cell and gene therapies) and stages of drug discovery (from target discovery, activity modeling, efficacy, and safety, to manufacturing and post-marketing safety monitoring and drug repurposing).

We are seeking outstanding postdoctoral research fellows and students, machine learning and data specialists, biomedical AI fellows, and an AI community manager who will lead research in AI to advance molecular drug design and clinical drug development.

Applications are reviewed on a rolling basis. Interested candidates are encouraged to submit their applications as soon as possible.

NOW OPEN: Request For Applications

Postdoctoral research fellows in foundation AI

We have multiple openings for postdoctoral research fellows in the broad area of foundation models focusing on geometric deep learning, multimodal learning, large-scale knowledge graphs, large language models, generative AI, and AI agents.

Applications are reviewed on a rolling basis. Interested candidates are encouraged to submit their applications as soon as possible.

NOW OPEN: Request For Applications

Postdoctoral research fellows in medical AI

We have an opening for a postdoctoral research fellowship in novel methods in the broad area of medical AI.

This position is available immediately. Interested candidates are encouraged to submit their applications as soon as possible.

NOW OPEN: Request For Applications

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

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

Generally, we expect:

  • Students commit 20 hours per week to research.
  • 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. 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 prioritize applications that include visits of six months or more in order to carry out a high-quality 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

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!

Mar 2024:   PocketGen - Generating Full-Atom Ligand-Binding Protein Pockets

PocketGen is a deep generative model that generates residue sequence and full-atom structure of protein pockets, maximizing binding to ligands. Project website.

Feb 2024:   SPECTRA - Generalizability of Molecular AI

Feb 2024:   Kaneb Fellowship Award

The lab receives the John and Virginia Kaneb Fellowship Award at Harvard Medical School to enhance research progress in the lab.

Feb 2024:   NSF CAREER Award

The lab receives the NSF CAREER Award for our research in geometric deep learning to facilitate algorithmic and scientific advances in therapeutics.

Feb 2024:   Dean’s Innovation Award in AI

Jan 2024:   AI's Prospects in Nature Machine Intelligence

We discussed AI’s 2024 prospects with Nature Machine Intelligence, covering LLM progress, multimodal AI, multi-task agents, and how to bridge the digital divide across communities and world regions.

Jan 2024:   Combinatorial Therapeutic Perturbations

New paper introducing PDGrapher for combinatorial prediction of chemical and genetic perturbations using causally-inspired neural networks.

Nov 2023:   Next Generation of Therapeutics Commons

Oct 2023:   Structure-Based Drug Design

Geometric deep learning has emerged as a valuable tool for structure-based drug design, to generate and refine biomolecules by leveraging detailed three-dimensional geometric and molecular interaction information.

Oct 2023:   Graph AI in Medicine

Graph AI models in medicine integrate diverse data modalities through pre-training, facilitate interactive feedback loops, and foster human-AI collaboration, paving the way to clinically meaningful predictions.

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