Adjunct Instructor in Generative AI in Clinical Decision Support

Requirements

• Required:
• 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 in addition to a strong grasp of the ethical implications of its 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.
• Preferred:
• 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)
• Interested candidates should submit:
• A cover letter highlighting relevant qualifications and teaching experience.
• A current CV or resume.
• Contact information for three professional references.
• Application review begins June 1, 2026 though we will continue to accept submissions on an ongoing basis.
• This appointment is to a position that is in a collective bargaining unit represented by SEIU Local 509.
• Compensation for this positon is: $6573.15
• Pay Range Disclosure
• Equal Opportunity Statement

Benefits

Additional Information

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. Core Course Responsibilities Summary Course Logistics and Facilitation : Focuses on the organized and timely rollout of course content, maintaining consistent communication through weekly announcements, and ensuring all instructional activities occur within university-approved digital platforms. Instructor Presence and Engagement : Centers on building an active teaching persona by hosting live introductory sessions, facilitating weekly academic discourse in forums, and maintaining regular availability for student consultation. Individual Feedback and Grading : Emphasizes the professional obligation to provide transparent, rubric-based evaluations and supportive commentary on student work within a standardized weekly timeframe. Professional Conduct and Standards : Requires adherence to university communication protocols, the promotion of respectful online "netiquette," and ensuring the course meets accessibility and technical visibility standards before and during the term.

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