Postdoctoral Fellow in Machine Learning and Multimodal AI — Brigham and Women’s Hospital / Harvard Medical School

id="offer-description" class="ecl-u-type-heading-2 ecl-u-mt-none ecl-u-mb-l">Offer Description The Kovacheva Lab at Brigham and Women’s Hospital and Harvard Medical School in Boston is recruiting a postdoctoral fellow in AI and machine learning. We are looking for a strong, independent computational researcher who wants to develop original machine learning methods, shape their research direction, and collaborate closely with clinicians on meaningful healthcare problems. Research opportunities . Our research integrates physiological signals, medical imaging, clinical text, and electronic health records, with applications in maternal and perioperative medicine. Potential directions include: . Physiological time-series modeling: ECG, PPG, and hemodynamic waveforms, including temporal patterns and irregularly sampled clinical data. Computer vision and representation learning: Ultrasound and other medical imaging modalities. Multimodal learning: Integrating physiological signals, images, clinical text, and structured electronic health records. Robust and trustworthy ML: Model development, rigorous validation, generalizability, and translation to clinical prediction tasks. Specific projects will be developed jointly around the fellow's methodological strengths and research interests, aligned with the lab's funded priorities. What makes this opportunity distinctive? . Our lab combines close clinical collaboration, access to large-scale real-world clinical datasets, and established r