Adjunct Instructor in Generative AI in Clinical Decision Support

Brandeis University•Waltham, MA

7d•Remote

About The Position

Brandeis University’s Online Health Informatics Program is seeking an Adjunct Faculty member for RHIN 175 Generative AI in Clinical Decision Support for the Fall-2 2026 session. This 3-credit asynchronous online course is an 8-week requirement for the Master of Science in Health Informatics. This course includes an examination of Large Language Models (LLMs) and generative artificial intelligence (AI) for clinical support: pilot design, prompt engineering, validation, safety, monitoring, and regulatory considerations. The course will also teach students how to assess deployment and impacts of AI in clinical workflows.

Requirements

Advanced degree (Master’s or Ph.D.) in Computer Science, Health Informatics, Data Science, Engineering, or a related field
Strong knowledge of AI fundamentals and its application to healthcare
Strong grasp of the ethical implications of AI usage
Minimum of 3 years of professional experience in developing and deploying real-world AI solutions
Proficiency in a programming language (e.g. Python, R)
At least 1 year of teaching or training experience (preferably online/asynchronous)
Experience with online instruction
Excellent communication and teaching skills in an online learning environment.

Nice To Haves

Prior online teaching experience at the graduate level
Knowledge of global learner personas and culturally responsive pedagogy
Familiarity with Moodle LMS and digital authoring tools (e.g., H5P)

Responsibilities

Organized and timely rollout of course content.
Maintaining consistent communication through weekly announcements.
Ensuring all instructional activities occur within university-approved digital platforms.
Building an active teaching persona by hosting live introductory sessions.
Facilitating weekly academic discourse in forums.
Maintaining regular availability for student consultation.
Provide transparent, rubric-based evaluations and supportive commentary on student work within a standardized weekly timeframe.
Adherence to university communication protocols.
Promotion of respectful online "netiquette".
Ensuring the course meets accessibility and technical visibility standards before and during the term.