Assistant Professor of Physiology; Computational​/AI Focus

Assistant Professor of Physiology (Computational/AI Focus) (CoM#230426) 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.

• 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.

• 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.

• 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.

• Research Statement (including future funding plans)