Job Overview
MIT's Institute for Medical Engineering & Science (IMES) invites applications for a full-time Research Associate position in Machine Learning for Health - an immediate opening on a four-year NIH grant (2025-2029). The researcher will develop machine learning methods for latent representation learning and generative modeling from complex, multimodal, time-varying clinical data, including clinical time series and physiological signals. The goal is to inform sequential treatment decision-making and generate actionable insights with high potential impact in clinical medicine, with extensions to medical imaging and physiological waveforms. The successful candidate joins a multidisciplinary team at the interface of computational methods and clinical medicine, working on approaches with high translational value for patient care. Required: a PhD in computer science, machine learning, statistics, biomedical informatics, or a related field; a strong publication record in top-tier ML and AI venues; demonstrated ability to conduct independent, high-quality research; and familiarity with modern probabilistic inference methods. Expertise in signal processing, dynamical systems, and multimodal representation learning is a plus. This is a temporary, full-time research position ending August 31, 2027, with possible renewal subject to funding and performance; remote work within the US is possible, and U.S. work authorization is required. Compensation corresponds to approximately $68,000-$73,000 annualized full-time, set by qualifications and experience. Applicants should email a CV to the principal investigator's address listed on the posting, stating current affiliation, expected start timeline, and 2-3 representative publications and venues; applications are reviewed periodically.