Postdoctoral Research Fellows and Students in AI & Therapeutics

Overview

Prof. Marinka Zitnik invites applications for multiple Postdoctoral Research Fellowship and Research Associate positions at Harvard University.

We are building the next generation of Therapeutics Commons. Therapeutics Commons is a global open-science initiative aimed at facilitating access and evaluation of AI across therapeutic modalities (including small molecules, macro-molecules, cell and gene therapies) and stages of drug discovery (spanning from molecular design and target nomination to modeling efficacy, safety, manufacturing, and drug repurposing). The Commons lays the foundational groundwork for AI methods to contribute to the development of novel therapies and explores the potential of AI in advancing drug development.

We are seeking exceptional postdoctoral research fellows and students, machine learning and data specialists, biomedical AI fellows as well as an AI community manager who will lead research in AI to advance molecular drug design and clinical drug development. Our vision is to lay the foundations for AI-based molecular and clinical drug development, ultimately enabling AI to learn on its own and acquire knowledge autonomously through integration with experimental platforms and self-driving labs.

Selected candidates will spearhead research in foundation models, large-scale knowledge graphs, multimodal learning, and generative AI. A central focus will be on creating universal benchmarks, developing efficient AI agents, and using data-centric approaches to establish new data and evaluation hubs. Additionally, there will be a strong emphasis on leading research, educational, and outreach initiatives in collaboration with both national and international stakeholders to ensure responsible and ethical use of AI in drug development.

Interested candidates are invited to review our recent publications and research directions.

Qualifications

We are seeking highly motivated applicants with backgrounds in one or more of the following areas: efficient machine learning systems, data-centric AI, generative and foundation models, ML benchmarks, and large-scale AI evaluation.

We are specifically looking for applicants who can demonstrate strong research skills, ideally with a track record of multiple publications in top-tier venues in machine learning and/or scientific/medical journals.

Candidates must hold a Ph.D. or an equivalent degree in computer science or a closely related field. Outstanding candidates with a Bachelor’s or Master’s degree will be considered. Strong programming skills and practical experience with leading deep learning frameworks are required.

Experience in applications of AI to molecular and clinical drug development is a strong plus. Successful candidates will have a track record of creating efficient and scalable models and/or datasets that are used by other scientists in the field.

Application process

Positions are 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 Research Fellows and Students in AI & Therapeutics”:

  • Curriculum Vitae
  • Links to your GitHub repositories, data and model hubs, and/or open-science initiatives
  • Two representative publications (preprints are acceptable)
  • Statement of research (max three pages) describing
    • Your current research and future research plans
    • Your expected contributions in creating the next generation of Therapeutics Commons
  • Three letters of recommendation (will be solicited after the initial review)

We are currently reviewing applications. Interested candidates are encouraged to submit their applications early.

Advisor

Marinka Zitnik is an Assistant Professor in the Department of Biomedical Informatics at Harvard Medical School, Kempner Institute for the Study of Natural and Artificial Intelligence, Broad Institute of MIT and Harvard, and Harvard Data Science. We investigate machine learning with a current focus on learning systems informed by geometry, structure, and symmetry and grounded in knowledge. This approach creates foundational models, including pre-trained, self-supervised, multi-purpose, and multi-modal models trained at scale to enable broad generalization. Our methods produce actionable outputs to advance medical problems past the state of the art and open up new opportunities.

Dr. Zitnik has published extensively in top ML venues, such as NeurIPS, ICLR, ICML, and leading scientific journals, including Nature, Nature Methods, Nature Communications, and PNAS. She has organized numerous workshops and tutorials in the nexus of AI, deep learning, AI4Science and AI4Medicine at leading conferences, where she is also in the organizing committees.

Her research received best paper and research awards from International Society for Computational Biology, International Conference on Machine Learning, Bayer Early Excellence in Science Award, Amazon Faculty Research Award, Google Faculty Research Scholar Award, Roche Alliance with Distinguished Scientists Award, Sanofi iDEA-iTECH Award, Rising Star Award in Electrical Engineering and Computer Science (EECS), and Next Generation Recognition in Biomedicine, being the only young scientist with such recognition in both EECS and Biomedicine. Dr. Zitnik was named Kavli Fellow 2023 by the National Academy of Sciences.

Dr. Zitnik is an ELLIS Scholar in the European Laboratory for Learning and Intelligent Systems (ELLIS) Society. She is a member of the Science Working Group at NASA Space Biology. Dr. Zitnik co-founded Therapeutics Data Commons and is the faculty lead of the AI4Science initiative. Dr. Zitnik is the recipient of the 2022 Young Mentor Award at Harvard Medical School.


Harvard is an Equal Opportunity Employer.

Latest News

Feb 2024:   Kaneb Fellowship and Dean’s Innovation Award

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.

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.

Sep 2023:   New papers accepted at NeurIPS

Sep 2023:   Future Directions in Network Biology

Excited to share our perspectives on current and future directions in network biology.

Aug 2023:   Scientific Discovery in the Age of AI

Jul 2023:   PINNACLE - Contextual AI protein model

PINNACLE is a contextual AI model for protein understanding that dynamically adjusts its outputs based on biological contexts in which it operates. Project website.

Jun 2023:   Our Group is Joining the Kempner Institute

Excited to join Kempner’s inaugural cohort of associate faculty to advance Kempner’s mission of studying the intersection of natural and artificial intelligence.

Jun 2023:   Welcoming a New Postdoctoral Fellow

An enthusiastic welcome to Shanghua Gao who is joining our group as a postdoctoral research fellow.

Jun 2023:   On Pretraining in Nature Machine Intelligence

May 2023:   Congratulations to Ada and Michelle

Congrats to PhD student Michelle on being selected as the 2023 Albert J. Ryan Fellow and also to participate in the Heidelberg Laureate Forum. Congratulations to PhD student Ada for being selected as the Kempner Institute Graduate Fellow!

Apr 2023:   Universal Domain Adaptation at ICML 2023

New paper introducing the first model for closed-set and universal domain adaptation on time series accepted at ICML 2023. Raincoat addresses feature and label shifts and can detect private labels. Project website.

Apr 2023:   Celebrating Achievements of Our Undergrads

Undergraduate researchers Ziyuan, Nick, Yepeng, Jiali, Julia, and Marissa are moving onto their PhD research in Computer Science, Systems Biology, Neuroscience, and Biological & Medical Sciences at Harvard, MIT, Carnegie Mellon University, and UMass Lowell. We are excited for the bright future they created for themselves.

Apr 2023:   Welcoming a New Postdoctoral Fellow

An enthusiastic welcome to Tianlong Chen, our newly appointed postdoctoral fellow.

Apr 2023:   New Study in Nature Machine Intelligence

New paper in Nature Machine Intelligence introducing the blueprint for multimodal learning with graphs.

Mar 2023:   Precision Health in Nature Machine Intelligence

New paper with NASA in Nature Machine Intelligence on biomonitoring and precision health in deep space supported by artificial intelligence.

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