Postdoc Openings in Machine Learning for Health at MIT

Jobs
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Dear All,

I am recruiting postdocs in machine learning for health to join 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. To apply, please see the following link: MIT Research Positions in Machine Learning for Health

Thanks,

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.

  • 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.

Application

Applicants should send a CV and expected timeline for starting the position to Li-wei Lehman (lilehman@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/

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Need a PhD student? Experienced dev but could never make the cut. I can do a few maths or two. I need to know a-priori if I’ll get accepted. It’s a waste of time/money to apply for these things where there’s no result. I have a lot of experience in clinical data analysis but a lot is just linear regression models, but I have worked on custom Bayesian models for biostatistics applications and forecasting models, etc. I mostly backend quantitative software. I was with SAS for ~=3 years with scientific computing and econometrics department. I can confidently build models that predict well and provide good inferences. Wanna chat? At UESTC in China, I was working on radar, which is a lot of convex optimization but they were having me use ANN’s as a my research project. 70 page thesis. andrezapico@gmail.com.