Postdoctoral Research Fellowship – Medical Artificial Intelligence

Mass General Brigham • Boston, MA

23h • $71,750 - $73,544 • Hybrid

About The Position

The NEIL AND ELISE WALLACE STRATUS Center for Medical Simulation at Brigham and Women's Hospital advances medical education, patient safety, and healthcare outcomes through medical simulation, innovative curricula, contemporary educational methods, continuous improvement, and multidisciplinary research. The Medical AI & Cognitive (MAICE) Lab, led by Dr. Roger Dias, brings together clinicians, engineers, psychologists, computer scientists, and educators to use technology to measure and improve clinician performance. Its research uses machine learning and artificial intelligence in areas including emergency medicine, critical care, surgery, space, and military medicine.

This full-time, 40-hour-per-week hybrid fellowship prepares researchers to bridge artificial intelligence and clinical care. Applicants apply to either the Computational Track, for doctoral graduates in STEM with advanced AI/ML foundations and healthcare or biomedical research experience, or the Clinician-Scientist Track, for clinicians with research experience and an interest in medical AI. The Clinician-Scientist Track is a research position with no clinical duties. Fellows work in interdisciplinary teams and receive mentorship across medicine, human factors, cognitive science, behavioral sciences, and aerospace and military research. Individual development plans support first-author publications, conference presentations, and applications for independent fellowships and career development awards.

The appointment is for two years, with the possibility of renewal for a third and/or fourth year subject to performance review and funding. The annual salary range is $71,750 to $73,544. Benefits include comprehensive health insurance, optional dental, vision, and retirement plans, and reimbursement for conference travel related to fellowship projects. The research fellow is eligible for an appointment at Harvard Medical School. Anticipated start date is February 2027, flexible; applications are reviewed on a rolling basis beginning October 2026 and accepted until the position is filled. Applications must be submitted by email to the hiring manager, not through Workday.

Requirements

• Doctoral degree (Ph.D., M.D., or equivalent) completed by the start date.
• At least three peer-reviewed publications as a significant contributor, including at least one lead-author publication. Journal articles and full-length peer-reviewed conference papers count.
• Strong interest in a research career at the intersection of AI and medicine.
• Excellent written and verbal communication skills and ability to work effectively in multidisciplinary teams.
• Computational Track: Ph.D. in a STEM discipline, such as computer science, electrical or biomedical engineering, data science, or statistics.
• Computational Track: proficiency in Python and/or C++.
• Computational Track: demonstrated experience training and evaluating AI/ML models, including neural networks and/or large language models.
• Computational Track: demonstrated experience with PyTorch and/or TensorFlow and/or scikit-learn.
• Computational Track: familiarity with signal processing and time-series analysis.
• Clinician-Scientist Track: M.D., D.O., Ph.D., or equivalent clinical degree.
• Clinician-Scientist Track: demonstrated clinical research experience, including study design, data collection, and statistical analysis.
• Clinician-Scientist Track: hands-on experience analyzing research data using R, Python, Stata, SAS, or a similar tool.
• Clinician-Scientist Track: demonstrated engagement with AI, data science, or digital health, such as an AI/ML research project, evaluation of AI tools or large language models, or relevant coursework or certificate.

Nice To Haves

• Computational Track: prior experience applying AI/ML to medical or health data.
• Computational Track: experience with computer vision, such as convolutional neural networks, and/or vision-language models.
• Computational Track: familiarity with cloud computing services and APIs.
• Computational Track: experience with multimodal data, such as video, audio, and physiological signals, or real-time systems.
• Clinician-Scientist Track: completed residency or other clinical training, especially in emergency medicine, critical care, anesthesiology, or surgery.
• Clinician-Scientist Track: advanced training in research methods, epidemiology, biostatistics, or informatics, such as an MPH, MS, MMSc, or Ph.D.
• Clinician-Scientist Track: programming experience in Python, including use of machine learning libraries.
• Clinician-Scientist Track: experience writing IRB protocols, conducting prospective data collection, or conducting healthcare simulation research.

Responsibilities

• Conduct medical AI research and development from study design through publication in settings such as emergency medicine, critical care, surgery, space, and military medicine.
• Work with large, multisource clinical datasets, including physiological time-series data, demographics, behavioral and psychological assessments, video, audio, electronic health records, and clinical performance outcomes.
• Contribute to developing and evaluating AI-based medical simulation applications and real-time clinical decision support systems.
• Lead first-author manuscripts and conference presentations and contribute to grant proposals.
• Collaborate with clinicians, engineers, and scientists, and help mentor students and junior team members.
• For the Computational Track: design and develop software architectures and data pipelines for AI-based solutions; train, fine-tune, and evaluate machine learning and AI models, including neural networks, large language models, and computer vision models; and integrate models with clinical databases and deploy them in simulation and real-time clinical environments.
• For the Clinician-Scientist Track: identify clinically meaningful problems and translate them into AI research questions and study designs; design and conduct simulation-based and clinical studies, including writing IRB protocols and leading data collection; define clinical ground truth through expert annotation of video, audio, physiological, and EHR data; evaluate AI systems for clinical accuracy, safety, bias, and usability, and study clinician interactions with AI; and build data analysis, programming, and AI skills through mentored projects and training.

Benefits

• Comprehensive health insurance.
• Optional dental plan.
• Optional vision plan.
• Optional retirement plans.
• Reimbursement for conference travel related to fellowship projects.
• Eligibility for an appointment at Harvard Medical School.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree