Postdoctoral Research Fellowship – Medical Artificial Intelligence

Description

The NEIL AND ELISE WALLACE STRATUS Center for Medical Simulation at BWH 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 prepares the next generation of researchers to bridge artificial intelligence and clinical care. Building AI that clinicians trust and use requires both technical depth and clinical insight, so the fellowship is open to candidates from two backgrounds who work side by side in interdisciplinary teams:

• Computational Track – for graduates of doctoral programs in Science, Technology, Engineering, and Mathematics (STEM) with a solid foundation in applying advanced ML/AI techniques. This track is ideal for early-career scientists seeking experience in the healthcare field and the opportunity to apply AI/ML and software engineering to improve patient care and safety. Prior experience in healthcare or biomedical research is required.
• Clinician-Scientist Track – for clinicians (M.D., D.O., PhD, or equivalent) with research experience and a strong interest in medical AI. This track is ideal for clinicians who want to lead the design, clinical validation, and implementation of AI tools while building hands-on data science and AI skills.

This is a full-time research position with no clinical duties. Fellows on both tracks are mentored by a multidisciplinary team of experts, gaining extensive knowledge and experience in diverse research areas including medicine, human factors, cognitive science, behavioral sciences, and aerospace and military research. Fellow follow an individualized development plan, with support for first-author publications, presentations at national and international conferences, and applications for independent fellowships and career development awards.

Responsibilities

All fellows will:

• Conduct research and development in the emerging field of medical AI, from study design through publication, in settings such as emergency medicine, critical care, surgery, space, and military medicine
• Work with large multi-source clinical datasets, including time-series physiological data, demographics, behavioral and psychological assessments, video, audio, electronic health records (EHR), and clinical performance outcomes
• Contribute to the development and evaluation of 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 closely with clinicians, engineers, and scientists, and help mentor students and junior team members

Computational Track fellows will focus on:

• Designing and developing software architectures and data pipelines for AI-based solutions
• Training, fine-tuning, and evaluating ML/AI models, including neural networks, large language models, and computer vision models
• Integrating AI models with large clinical databases and deploying them in simulation and real-time clinical environments

Clinician-Scientist Track fellows will focus on:

• Identifying clinically meaningful problems and translating them into AI research questions and study designs
• Designing and conducting simulation-based and clinical studies, including writing IRB protocols and leading data collection
• Defining clinical ground truth, including expert annotation of video, audio, physiological, and EHR data
• Evaluating AI systems, including large language models, for clinical accuracy, safety, bias, and usability, and studying how clinicians interact with AI tools
• Building hands-on skills in data analysis, programming, and AI through mentored projects and available training opportunities

Qualifications

Applicants must meet the requirements for all applicants plus those for their chosen track. Preferred qualifications are not required; we encourage you to apply even if you do not meet all of them.

Required for all applicants:

• Doctoral degree (Ph.D., M.D., or equivalent) completed by the start date
• At least three peer-reviewed publications in which the candidate was a significant contributor, including at least one as lead author. Both journal articles and full-length peer-reviewed conference papers (e.g., NeurIPS, ICML, MICCAI, CHI, ML4H) count.
• Strong interest in a research career at the intersection of AI and medicine
• Excellent written and verbal communication skills and the ability to work effectively in multidisciplinary teams

Required for the Computational Track:

• Ph.D. in a STEM discipline (e.g., computer science, electrical or biomedical engineering, data science, statistics)
• Proficiency in Python and/or C++
• Demonstrated experience with AI/ML model training and evaluation, including neural networks and/or large language models
• Demonstrated experience with PyTorch and/or TensorFlow and/or scikit-learn
• Familiarity with signal processing and time series analysis

Preferred for the Computational Track:

• Prior experience applying AI/ML to medical or health data
• Experience with computer vision (e.g., convolutional neural networks) and/or vision-language models
• Familiarity with cloud computing services and APIs
• Experience with multimodal data (e.g., video, audio, and physiological signals) or real-time systems

Required for the Clinician-Scientist Track:

• M.D., D.O., Ph.D., or equivalent clinical degree
• Demonstrated clinical research experience, including study design, data collection, and statistical analysis
• Hands-on experience analyzing research data using R, Python, Stata, SAS, or a similar tool
• Demonstrated engagement with AI, data science, or digital health, such as a research project that used AI/ML methods, an evaluation of AI tools or large language models, or coursework or a certificate in data science, AI, or clinical informatics

Preferred for the Clinician-Scientist Track:

• Completed residency or other clinical training, especially in emergency medicine, critical care, anesthesiology, or surgery
• Advanced training in research methods, epidemiology, biostatistics, or informatics (e.g., MPH, MS, MMSc, or Ph.D.)
• Programming experience in Python, including use of ML libraries
• Experience writing IRB protocols, conducting prospective data collection, or doing healthcare simulation research