Faculty - Department of Biomedical Informatics, Division of Artificial Intelligence in Digital Health, Artificial Intelligence (Open Rank Tenure-Track)

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Job Title:

Faculty - Department of Biomedical Informatics, Division of Artificial Intelligence in Digital Health, Artificial Intelligence (Open Rank Tenure-Track)

Department:

Medicine | School Biomed Sci - Biomedical Informatics

Tenure faculty (regardless of rank) - persons with the titles of professor, associate professor, assistant professor and instructor who serve on appointments totaling 50% or more service to the university and who are eligible for tenure or who have obtained tenure. Duties and responsibilities are assigned in accordance with the workload guidelines laid out in the pattern of administration of each faculty member’s tenure initiation unit (TIU) and, as appropriate, regional campus; obligations will include research, service and/or teaching or clinical practice.  These faculty will have earned doctorate or other terminal degree in the relevant field of study or possession of equivalent experience.

Artificial Intelligence in Digital Health (AIDH, https://go.osu.edu/aidh) in the Department of Biomedical Informatics (BMI, http://bmi.osu.edu) at The Ohio State University (OSU) is currently seeking applicants for multiple open rank (Assistant/Associate/Full) tenure-track faculty in Artificial Intelligence (AI), starting as early as Fall 2026. Academic rank and track commensurate with academic record and experience.

Position Overview

Candidates with expertise and interests in predictive modeling, computer vision, natural language processing, generative AI, embodied AI, and/or multimodal learning are encouraged to apply. Successful applicants should have a Ph.D. in Computer Science, Artificial Intelligence, Data Science, Biomedical Informatics or a related discipline, with scientific collaboration and research interests in one or more of the following areas:

• Foundation Models and Generative AI, including designing, pre-training, or fine-tuning large-scale Large Language Models (LLMs) and Vision-Language Models (VLMs), Parameter-Efficient Fine-Tuning (PEFT), and alignment strategies (e.g., Reinforcement Learning from Human Feedback [RLHF]) for healthcare and life sciences.
• AI Agents and Autonomous Reasoning, including the development of autonomous agents capable of multi-step clinical reasoning and planning, as well as systems that utilize Retrieval-Augmented Generation (RAG) to transform unstructured biomedical text into computable, auditable evidence.
• Embodied AI and Ubiquitous Computing, including medical robotics, intelligent physical systems, autonomous control, and resource-efficient edge AI models for continuous digital phenotyping on wearables and mobile device