Assistant Professor of Physiology Computational/AI Focus CoM

Assistant Professor of Physiology (Computational/AI Focus) (CoM) Job Summary

The Department of Physiology invites applications for a full-time, Assistant Professor position. We seek a dynamic, innovative researcher utilizing Large Language Models (LLMs), Generative AI, or advanced natural language processing (NLP) to advance mechanistic understanding in Neuroscience, or Gastrointestinal/Endocrinology, or Reproduction, or Respiratory Physiology. The successful candidate will establish an externally funded research program, contribute to high-quality teaching, and leverage computational approaches to revolutionize physiological research.

Key Responsibilities

• Research: Establish and maintain a vigorous, independently funded research program focusing on the application of Large Language Models (LLMs) to physiological data, such as mining literature for mechanistic insights, modeling system interactions, or developing AI driven diagnostic/predictive tools in their specialized field (Neuro/GI/Repro/Resp).
• Teaching: Deliver high-quality instruction in physiology courses to graduate and professional (medical/dental) students. Develop curriculum incorporating computational physiology and AI literacy.
• Mentorship: Mentor graduate students, postdoctoral fellows, and undergraduate trainees in both traditional lab techniques and computational methodology.
• Service: Participate in departmental, university, and professional society committees. Collaborate with clinical departments to foster translational AI initiatives.

Required Qualifications

• Ph.D. in Physiology, or Neuroscience, or Gastrointestinal/Endocrinology, or Reproduction, or Respiratory Physiology or Computational Biology, Data Science, or a closely related discipline.
• Demonstrated expertise in applying Large Language Models (LLMs), AI, or machine learning to biomedical datasets.
• A strong record of research publications in high-impact physiology or computational journals.
• Evidence of, or strong potential to obtain, extramural funding.
• Excellent written and verbal communication skills.

Preferred Qualifications

• Postdoctoral training in a physiology-related field.
• Experience in bridging traditional bench top physiology with AI/computational modeling.
• Prior teaching experience in medical or graduate level physiology is preferred.

Application Instructions

• Research Statement (including future funding plans)