Assistant Professor of Physiology

The Department of Physiology invites applications for a full-time, tenure-track 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 (60-70%): 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 (20-30%): Deliver high-quality instruction in physiology courses to graduate and professional (medical/dental) students.

Develop curriculum incorporating computational physiology and AI literacy.

Mentorship (10%): 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.

in bridging traditional bench-top physiology with AI/computational modeling.

Prior teaching experience in medical or graduate-level physiology is preferred.