MIT Research Positions in Machine Learning for Health

I am looking for highly-motivated post-docs to join my team at the Institute for Medical Engineering & Science (IMES) at MIT starting in Spring 2026. Positions will remain open until filled. Openings are available for the following positions:

MIT Postdoctoral Position in Machine Learning for Health

The Massachusetts Institute of Technology (MIT), Institute for Medical Engineering & Science (IMES) invites applications for a Postdoctoral Associate position in Machine Learning for Health. This is an immediate opening for a highly motivated researcher interested in developing machine learning methods for latent representation learning and generative modeling from complex, multimodal, time-varying clinical data, with the goal of informing sequential treatment decision-making and generating actionable insights with high potential impact in clinical medicine.

The project offers opportunities to develop and apply novel machine learning and statistical approaches to generate clinically meaningful insights from observational health data, including clinical time series and physiological signals, with extensions to multimodal learning from medical imaging, physiological waveforms, and physician text reports. The successful candidate will join a multidisciplinary team working at the interface of computational methods and clinical medicine to develop approaches with high translational value that inform patient care and treatment decisions.

Qualifications

• Ph.D. in Computer Science, Machine Learning, Statistics, or a related field
• Strong publication record in top-tier machine learning and AI venues
• Demonstrated ability to conduct independent, high-quality research

Preferred Expertise

• Probabilistic and generative modeling (e.g., latent variable models, deep generative models, and scalable inference methods)
• Representation learning (interpretability, structure discovery from time-varying data)
• Dynamic systems and state-space modeling. (e.g., deep dynamical systems, state-space models)
• Additional expertise in one or more of the following is advantageous: sequential decision making, offline reinofrcement learning, multimodal representation learning, causal inference, and robust modeling under distributional shifts.

Application

Applicants should send a CV and a brief description of research interests, and expected timeline for starting the position to Li-wei Lehman (lilehman@mit.edu). Applications will be reviewed periodically, and candidates whose background and expertise are a strong fit for the position will be contacted for next steps.

Position Description

Duties include conducting original research, publishing in top-tier machine learning conferences and scientific journals, mentoring students, and contributing to research grant proposal writing. This position is budgeted at an annual salary of $71K.

Li-wei Lehman, Ph.D.
Research Scientist
Institute for Medical Engineering & Science
Massachusetts Institute of Technology
http://web.mit.edu/lilehman/www/

MIT Postdoc Associate in Machine Learning and Sequential Decision Making for Health

The Massachusetts Institute of Technology (MIT), Institute for Medical Engineering & Science (IMES) invites applications for a Postdoctoral Associate position in Machine Learning and Sequential Decision Making for Health. This is an immediate opening for a highly motivated researcher with strong background in machine learning and sequential decision making to work on developing approaches for time-varying, multimodal clinical data, supporting sequential treatment decision-making and generating actionable clinical insights.

The project provides opportunities to develop and apply state-of-the-art machine learning and statistical methods to large, multimodal, time-varying observational data from electronic health records. The successful candidate will join a multidisciplinary team working at the interface of computational methods and clinical medicine, contributing to projects with high translational impact on healthcare.

Qualifications

• Ph.D. or advanced degree in Computer Science, Machine Learning, Data Science, Statistics, Medical Informatics, or a related field.
• Strong background in machine learning or statistical modeling.
• Experience in working with real-world health datasets preferred.
• Solid publication record in peer-reviewed venues in machine learning, AI, or biomedical data science.

Preferred Expertise (one or more)

• Machine learning for health and sequential decision making.
• Model-based offline reinforcement learning; off-policy policy evaluation; planning under uncertainty.
• Probabilistic and generative modeling; representation learning for multimodal data.
• Additional expertise in one or more of the following is advantageous but not required: causal inference and dynamic treatment regimes, dynamical systems for controls, policy learning, robust modeling under distributional shifts.

Application

Applicants should send a CV, a brief description of research interests, and expected timeline for starting the position to Li-wei Lehman (lilehman@mit.edu). Applications will be reviewed periodically, and candidates whose background and expertise are a strong fit for the position will be contacted for next steps.

Position Description

Duties include conducting original research, developing and evaluating machine learning models on clinical and physiological data, publishing in top-tier machine learning conferences and scientific journals, mentoring students, and contributing to research grant proposal writing. This position is budgeted at an annual salary of $71K.

Li-wei Lehman, Ph.D. 
Research Scientist 
Institute for Medical Engineering & Science
Massachusetts Institute of Technology
http://web.mit.edu/lilehman/www/