[JOBS] Postdoc Opportunity in Machine Learning for Health at MIT

[lilehman]

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. 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).
• Dynamical systems and state-space models (probabilistic and deep state-space models, switching state-space models).
• Representation learning for sequential decision making (interpretability, structure discovery from time-varying data).
• Expertise in one or more of the following is advantageous but not required: representation learning from multimodal data, model-based off-line reinforcement 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 (lile...@mit.edu) asap. Applications will be reviewed periodically, and candidates whose background and expertise are a strong fit for the position will be contacted for next steps.

For more details, please see http://web.mit.edu/lilehman/www/postdoc.html

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 or a related field.
• Strong background in machine learning or statistical modeling.
• Solid publication record in top-tier machine learning venues.

Preferred Expertise 

• Machine learning for sequential decision-making.
• Target trial emulation and counterfactual reasoning from observational data.
• Development of interpretable models to support reliable decision-making in high-stakes settings.
• Incorporation of inductive biases, structure, or domain knowledge to enable robust sequential decision-making.
• Additional expertise in one or more of the following is advantageous: dynamical systems and control, model-based offline reinforcement learning, off-policy policy evaluation, foundation models for decision-making, robust modeling under distributional shift, and causal inference for dynamic treatment regimes.

Application

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

For more details, please see http://web.mit.edu/lilehman/www/postdoc.html

Li-wei Lehman, Ph.D.

Research Scientist

Institute for Medical Engineering & Science

Massachusetts Institute of Technology

http://web.mit.edu/lilehman/www/