MIT Research Positions in Machine Learning for Health
The Massachusetts Institute of Technology (MIT), Institute for Medical Engineering & Science (IMES) invites applications for full-time research positions in Machine Learning for Health. These roles are for highly motivated researchers 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 projects offer 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 and physiological waveforms. Successful candidates 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. Positions are supported by a four-year NIH grant (2025-2029).
Designation
- Postdoc: Initial appointment for up to 11 months through August 31st, 2027, with possibility of annual renewal. Annual salary $73,000.
- Research Associate: Temporary, full-time research position, with possibility of remote work within the US. Renewal possible. Compensation rate approximately $68,000–$73,000 on an annualized full-time basis, depending on qualifications and experience.
- Postdoctoral Associate: For highly motivated researchers with a strong background in machine learning and sequential decision-making. Annual salary $73,000.
Research Area
- Machine Learning for Health
- Latent representation learning and generative modeling from complex, multimodal, time-varying clinical data.
- Informing sequential treatment decision-making and generating actionable insights in clinical medicine.
- Applying novel machine learning and statistical approaches to observational health data (clinical time series, physiological signals, medical imaging, physiological waveforms).
- Representation learning from time-varying, multimodal data, with an emphasis on interpretable latent structure discovery and latent state representation learning for forecasting and sequential decision-making.
- Dynamic systems and state-space modeling, including deep state-space models and switching dynamical systems.
- Probabilistic machine learning, including latent variable models, approximate inference, variational autoencoders (VAEs), structured VAEs, and related deep latent variable models.
- Incorporation of inductive biases, structural constraints, or domain knowledge into ML models to improve robustness and generalization.
- Model-based offline reinforcement learning; off-policy policy evaluation.
- Counterfactual reasoning from observational data.
- Interpretable and trustworthy models for high-stakes decision-making.
- Dynamical systems, foundation models for decision making, causal inference.
Location
Massachusetts Institute of Technology (MIT), Institute for Medical Engineering & Science (IMES), USA. Remote work within the US may be possible for Research Associate roles.
Eligibility/Qualification
For Postdoc / Research Associate:
- Ph.D. in Computer Science, Machine Learning, Statistics, Biomedical Informatics, or a related field.
- Strong publication record in top-tier machine learning and AI venues.
- Demonstrated ability to conduct independent, high-quality research.
- Familiarity with modern probabilistic inference methods.
- Publications in top-tier AI/ML and/or ML for Health venues.
- Expertise in signal processing, dynamical systems, and multimodal representation learning a plus.
- Candidates for Postdoc and Research Associate must currently have authorization to work in the US. Visa sponsorship is not available for these specific roles.
For Postdoctoral Associate:
- Ph.D. or advanced degree in Computer Science, Machine Learning, Data Science, Statistics, or a related field.
- Strong background in machine learning or statistical modeling.
- Solid publication record in top machine learning venues.
- Preferred Expertise:
- Machine learning for health and sequential decision making.
- Model-based offline reinforcement learning; off-policy policy evaluation.
- Counterfactual reasoning from observational data.
- Interpretable and trustworthy models for high-stakes decision-making.
- Incorporation of inductive biases, structure, or domain knowledge for robust learning.
- Experience in Dynamical systems, foundation models for decision making, causal inference is advantageous.
- Experience with real-world health datasets is helpful.
- Publications in AI/ML venues preferred (e.g., NeurIPS, ICML, ICLR, AISTATS, UAI, AAAI, MLHC, ML4H, CHIL or equivalent).
Job Description
- Conduct original research in machine learning for health.
- Develop and evaluate machine learning models on clinical and physiological data.
- Publish findings in top-tier conferences and journals.
- Mentor students.
- Contribute to research grant proposal writing.
How to Apply
Applicants should send a CV to Li-wei Lehman (lilehman@mit.edu).
- Please specify your current affiliation.
- Indicate your expected timeline for starting the position.
- List 2–3 representative publications and venues.
- For Postdoctoral Associate roles, also include a brief description of research interests.
Last Date for Apply
Applications will be reviewed periodically, and candidates whose background and expertise are a strong fit will be contacted for next steps. There is no specified deadline, but early application is encouraged.
Apply Link
Email applications to: lilehman@mit.edu
