Postdoctoral Research Fellow in Machine Learning and Predictive Modeling


The Fellow will work in a multi-disciplinary team of machine learning scientists, informaticians, biostatisticians and clinicians led by Dr. Marinka Zitnik and Dr. Alexander Turchin on projects involving development of predictive models in medicine leveraging temporal and longitudinal data. Explainable AI models will be based on data from a large integrated healthcare system and will utilize a combination of artificial intelligence and natural language processing of multiple narrative document streams.


Brigham and Women’s Hospital and Harvard Medical School


We seek a highly motivated individual with background in deep learning and / or large-scale knowledge graphs and / or large language models; experience working with large datasets, ability to design and conduct effective research studies; strong analytical, scientific writing, presentation and communication skills; strong programming and system development skills; working knowledge of deep learning frameworks, such as PyTorch; and working knowledge of SQL.

Experience with predictive modeling, natural language processing, medical terminologies and ontologies is a strong plus. Successful applicants will be strong technically as well as have an inclination towards real-world problems.

Candidates must have a Ph.D. or equivalent degree in computer science, statistics, biomedical informatics, computational biology or a closely related field.

Application process

The position is available immediately and can be renewed annually. Interested applicants should submit the following documents via email to Dr. Alexander Turchin and Prof. Zitnik and use the subject line “Postdoctoral Fellowship in Machine Learning / Predictive Modeling”:

  • Curriculum Vitae (please include links to your academic webpage and any software you developed, e.g., GitHub repositories)
  • Two representative publications (preprints are acceptable)
  • Statement of Research (2 pages) describing prior research experience and future research plans

Letters of recommendation will be solicited after the initial review. We are currently reviewing applications for this position. Interested candidates are encouraged to submit their applications as soon as possible.

Harvard is an Equal Opportunity Employer.

Latest News

Sep 2023:   New papers accepted at NeurIPS

Congratulations to Owen and Zaixi for having their papers accepted as spotlights at NeurIPS! These papers, which are among the top 3% of all submissions, focus on explaining sequence models through self-supervised learning and the full-atom design of protein pockets.

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.

Mar 2023:   Self-Driving Labs in Nature Machine Intelligence

Mar 2023:   TxGNN - Zero-shot prediction of therapeutic use

Mar 2023:   GraphXAI published in Scientific Data

Feb 2023:   Welcoming New Postdoctoral Fellows

A warm welcome to postdoctoral fellows Wanxiang Shen and Ruth Johnson. Congratulations to Ruthie for being named a Berkowitz Fellow.

Feb 2023:   New Preprint on Distribution Shifts

Feb 2023:   PrimeKG published in Scientific Data

Jan 2023:   GNNDelete published at ICLR 2023

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