[JOB] Postdoc Openings in Machine Learning for Health at MIT

[Li-wei Lehman]

Dear All,

I am pleased to announce postdoctoral openings in machine learning for health in my group at MIT. These positions offer the opportunity to develop and apply cutting-edge methods in probabilistic modeling, representation learning, and sequential decision-making, with potential for real-world clinical impact. Please help forward the following announcement to potential candidates.

Li-wei Lehman,

Research Scientist 

Institute for Medical Engineering & Science, 

Massachusetts Institute of Technology

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

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 to develop approaches with high translational impact in health and medicine.

Qualifications

• Ph.D. in Computer Science, Machine Learning, Statistics, or a related field
• Strong publication record in top-tier Machine Learning or ML for health venues. Please see website for details.
• Demonstrated ability to conduct independent, high-quality research

Preferred Expertise

• Probabilistic and generative modeling (e.g., latent variable models, deep generative models, and variational inference).
• Dynamical systems and state-space models (probabilistic and deep state-space models, switching state-space models).
• Representation learning (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, causal inference, model-based off-line reinforcement learning, and robust modeling under distributional shifts.

Applicants should send a CV and expected timeline for starting the position to Li-wei Lehman (lile...@mit.edu) asap. In the email, please state your current affiliation, your research interests, and a listing of 2-3 papers that are most representative of your work. 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.
• Strong publication record in top-tier Machine Learning or ML for health venues. See website for details.

Preferred Expertise 

• Machine learning for sequential decision-making.
• Counterfactual reasoning from time-varying observational data.
• Development of interpretable models to support reliable decision-making in high-stakes settings.
• Additional expertise in one or more of the following is advantageous: dynamical systems and control, model-based offline reinforcement learning, off-policy policy evaluation, robust modeling under distributional shift, and causal inference.

Application

Applicants should send a CV and expected timeline for starting the position to Li-wei Lehman (lile...@mit.edu) asap.  In the email, please state your current affiliation, your research interests, and a listing of 2-3 papers that are most representative of your work. 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/