Part-time Faculty - Health Data Science & AI

The Department of Health and Clinical Outcomes Research at Saint Louis University School of Medicine invites applications for Adjunct Assistant or Associate Professor positions to teach graduate-level courses and mentor students in applied health data science and artificial intelligence in medicine., Adjunct faculty will teach one or more of the following courses or related electives within the Departments data science and AI curriculum:

• Privacy, Ethics, Regulation & PolicyÂ
• Introduction to Artificial IntelligenceÂ
• Predictive Modeling and Machine LearningÂ
• Image Processing and Deep LearningÂ
• Bioinformatics & Biomedical / Clinical Data Analysis Natural Language Processing and Large Language ModelsÂ
• Reinforcement Learning for Clinical Decision MakingÂ
• Telehealth & Telemedicine
• AI for Precision Medicine & Genomics & DiagnosticsÂ
• Research in MedicineÂ

Additional responsibilities include:

• Develop and deliver engaging, practice-based course materials.
• Mentoring students on applied research and analytics projects using real-world datasets.
• Collaborating with program leadership to ensure content quality, alignment with learning objectives, and current best practices in AI and health data analytics.
• Participating in departmental meetings or student events, as appropriate for adjunct appointments.

Required:

• Ph.D. or equivalent terminal degree in Health Data Science, Biomedical Informatics, Computer Science, Biostatistics, or a related field.
• Demonstrated expertise in one or more areas of artificial intelligence, data science, or computational health analytics.
• Proficiency with relevant tools and languages (e.g., Python, R, SQL, TensorFlow, PyTorch, SAS).
• Strong communication skills and a commitment to high-quality, student-centered graduate teaching.

Preferred:

• Experience teaching graduate courses in AI, data science, or health informatics.
• Applied or research experience involving AI deployment in healthcare, biomedical, or clinical contexts.
• Familiarity with regulatory, ethical, and equity considerations in digital health and AI.
• Record of scholarly or professional contributions in data-driven healthcare innovation.