Postdoctoral Associate

POSTDOCTORAL ASSOCIATE, MACHINE LEARNING FOR HEALTH, Medical Engineering & Science (IMES), will develop machine learning methods for latent representation learning 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; and work closely with Dr. Li-wei Lehman and join a multidisciplinary team developing machine learning and statistical methods with strong translational impact in health and medicine. The project offers opportunities to develop and apply novel approaches to generate clinically meaningful insights from observational health data, including clinical time series and physiological signals, with potential extensions to multimodal learning.

REQUIRED: Ph.D. in Computer Science, Machine Learning or a related field; and strong publication record in top-tier machine learning venues; and expertise in probabilistic machine learning and familiarity with approximate inference methods for latent variable models. PREFERRED: Probabilistic machine learning, including latent variable models and approximate inference; dynamical systems and state-space models, including probabilistic and deep state-space models, latent state estimation, and switching state-space models; and representation learning from multimodal, time-varying data, including interpretability and latent structure discovery.

Applicants should upload a brief cover letter and CV as soon as possible. In the cover letter, please include: your current affiliation, your expected timeline for starting the position, a brief summary of your research interests, and a selected list of 2-3 representative papers, including their publication venues.