Postdoctoral Research Fellowship - Medical Artificial Intelligence

Please email all materials to the Fellowship Director, Roger Dias, MD, PhD, MBA  by July 31st, 2025.

The Neil and Elise Wallace STRATUS Center for Medical Simulation is committed to advancing medical education, patient safety, and healthcare outcomes through cutting‑edge applications of medical simulation technologies supported by innovative curricula, contemporary educational methodologies, continuous improvement, and rigorous, multidisciplinary research.

At STRATUS, the Medical AI & Cognitive (MAICE) Lab, led by Dr. Roger Dias, MD, PhD, MBA, brings together a multidisciplinary team of clinicians, engineers, psychologists, computer scientists, and educators, including collaborations with several scientists and institutions, nationally and internationally. The core mission of our research lab involves the use of cutting‑edge technologies to objectively measure and improve clinicians' performance, with the ultimate goal of enhancing patient safety and improving clinical outcomes.

Our projects have been funded by NASA, NIH, DoD, and NSF, leveraging machine learning and artificial intelligence to support high‑performance clinical care in a variety of settings, including emergency medicine, critical care, surgery, space, and military medicine.

The Postdoctoral Fellowship in Medical AI is intended for graduates of doctoral programs in Science, Technology, Engineering, and Mathematics (STEM) with a solid foundation in applying advanced ML/AI techniques to tackle complex problems in healthcare. The program is ideal for early‑career scientists seeking additional experience in the healthcare field, as well as opportunities to apply AI/ML technologies and software engineering to improve patient care and safety.

The research fellow will be mentored by a multidisciplinary team of experts, allowing her/him to gain extensive knowledge and experience in diverse research areas including medicine, human factors, cognitive science, behavioral sciences, and aerospace and military research.

The successful candidate will conduct research and development within the emerging field of medical AI, applying advanced programming and software engineering skills. The research fellow will design and develop software architectures and train and evaluate ML/AI models to create AI‑based solutions and integrate them with large multi‑source clinical databases, including time‑series physiological data, demographics, behavioral and psychological assessments, video, audio, electronic health records (EHR), and clinical performance outcomes.

The fellow will also develop AI‑based medical simulation applications and real‑time clinical decision‑support systems.

• Proficiency in Python and/or C++
• Familiar with signal processing and time series analysis
• Familiar with cloud computing services and API
• Demonstrated experience with AI/ML training and model evaluations, including neural networks and/or large language models
• Demonstrated experience with Tensor Flow and/or PyTorch and/or Scikit-learn
• Demonstrated prior experience in applying AI/ML in the medical field
• Demonstrates research experience as lead author in peer‑reviewed scientific publications

• Previous experience with computer vision (e.g., convolutional neural networks) and/or visual language models

The selected candidate will be appointed for two years, with the opportunity to renew the appointment for a third and/or fourth year pending performance review and funding. Salary will be commensurate with experience, based on BWH guidelines for postdoctoral trainees. Benefits include comprehensive health insurance, optional dental, vision, and retirement plans, and reimbursement for conference travel related to fellowship projects. BWH is a Harvard‑affiliated Teaching Hospital, and the research fellow with be eligible for an appointment at Harvard Medical School.

1. Current CV

2. Recent work sample (e.g., peer‑reviewed manuscript, pre‑print, Git Hub code, software demo)

3. At least three peer‑reviewed publications in which the candidate was a significant contributor

3. Candidates selected for an…