Description

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 devices.
• Multimodal Innovation, Trustworthy AI, and Clinical Translation, including scalable algorithms that integrate high-throughput molecular data, medical imaging, and Electronic Health Records (EHRs), focusing on privacy-preserving learning (e.g., federated learning), algorithmic security, widely adopted software tools, and integration into real-world clinical workflows.

Duties and Responsibilities
Successful candidates will work as part of a collaborative team with investigators across the health sciences campus to develop grant proposals and design studies for clinical trials, large cohorts, population research, basic science, and high-throughput omics at both The Ohio State University and Nationwide Children’s Hospital. Opportunities to develop methodologies, contributing to the academic mission through teaching in graduate programs, mentoring M.S. and Ph.D. students, and supervising research trainees are available and encouraged.

Successful candidates will also join the AI(X) Hub (https://go.osu.edu/ai-hub) at Ohio State, established to drive innovation, provide resources, and foster the development of foundational and applied AI. The AI(X) Hub will be organized around several pillars: AI Foundations, Health, Engineering and Sciences, Agriculture, Trustworthy AI, and Cybersecurity. The Hub’s Faculty Hiring Initiative will elevate Ohio State’s national reputation, specifically related to AI in addition to attracting top-tier academic talent and securing external funding. This advertised position will dovetail with similar positions in the soon-to-be-standing-up AI in Health Pillar of the AI(X) Hub. Successful candidates will have the opportunity to work closely with Health Pillar (primary) and AI Foundations Pillar (secondary). It is very likely that the research focus of the Health Pillar will include: (a) AI-Driven Therapeutics Discovery; (b) AI-Enabled Precision Medicine; (c) AI Implementation and Translation; (d) AI Education in Health and (e) Scalable and Robust AI Infrastructure for Health.

How to Apply

To be considered, please submit your application electronically via Workday. Application materials must include cover letter, a Curriculum Vita, a statement of research plans (limited to 3 pages), a statement of teaching and mentoring, and a list of three references. Any questions can be directed to the chair of the search committee, Dr. Qing Wu (Qing.Wu@osumc.edu). Evaluation of applications is underway and will continue until positions are filled.

For full details and to submit your application, please visit: https://go.osu.edu/ai_job

Requirements

Requirements
Applicants should have a Ph.D. in Computer Science, Artificial Intelligence, Data Science, Biomedical Informatics or a related discipline. Preference will be given to candidates who demonstrate methodological innovation in AI rather than routine application of existing tools.