Faculty Position in Clinical Informatics and Real-World Implementation of Artificial Intelligence
Full-time Instructor or Assistant Professor
The Computational Health Informatics Program (CHIP) at Boston Children's Hospital, an affiliate of Harvard Medical School, is recruiting research faculty at the Instructor or Assistant Professor level focused on the clinical implementation of artificial intelligence. We seek investigators who are building the evidence, methods, and infrastructure needed to bring AI and clinical information systems safely and effectively into real-world health care.
Successful candidates will have a record of research conducted in clinical or health system settings and a clear vision for improving care through the safe and effective use of AI. Areas of interest include prospective and pragmatic evaluation of AI-enabled tools in clinical workflow, implementation science applied to clinical decision support, post-deployment monitoring of model performance and unintended consequences, human factors and clinician interaction with AI, safety surveillance, standards-based integration with electronic health records, and the governance and regulatory questions that arise when AI enters clinical practice. Candidates will have access to rich clinical data resources, a health system actively deploying clinical AI, opportunities to partner with clinicians and health system leaders, and CHIP's long-standing work on interoperability and open standards for health data exchange.
CHIP, founded in 1994, is a multidisciplinary applied research and education program and a leader in biomedical informatics. Its faculty come from medicine, data science, computer science, epidemiology, implementation science, and health services research. CHIP is located in beautiful, newly designed space at 401 Park Drive, in the heart of the Fenway neighborhood of Boston, and surrounded by wonderful dining, entertainment, and sports.
Minimum qualifications. A doctoral degree (MD, PhD, MD/PhD, or equivalent). A record of peer-reviewed publication. Quantitative, computational, or methodological training sufficient to lead rigorous research. Research experience with artificial intelligence or machine learning in health care, as a developer, evaluator, or implementer. Research experience in real-world clinical or health system settings rather than retrospective datasets alone.
Additional qualifications we will weigh. Experience developing, validating, or deploying machine learning models. Formal training or demonstrated expertise in implementation science, dissemination research, health services research, or related fields. Training or experience in the design of prospective evaluations, such as randomized trials, pragmatic trials, stepped-wedge designs, and interrupted time series. Experience building or deploying interoperable software integrated with electronic health records. Experience partnering with clinical operations, quality and safety, or informatics governance. Current or prior research funding, including career development awards and foundation grants.
Faculty mentor students and trainees. An academic appointment at Harvard Medical School requires a minimum of 50 hours per year of teaching Harvard learners, which includes research supervision and mentoring of postdoctoral fellows and other trainees. Faculty are expected to secure independent funding to support their research.
How to apply. Submit a CV, a cover letter, and a research plan of no more than three pages to Kenneth D. Mandl, MD, MPH, CHIP Director, at contact "dot" chip "at" childrens "dot" harvard "dot" edu. Applicants should also arrange for three letters of reference to be sent directly to the same email address. Applications are due January 4, 2027.
Boston Children's Hospital strongly encourages women and minorities underrepresented in medicine to apply for this position. We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, gender identity, sexual orientation, pregnancy and pregnancy-related conditions or any other characteristic protected by law.
