Assistant/Associate Professor of Artificial Intelligence and Machine Learning in Veterinary Population Health

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Job Title:

Assistant/Associate Professor of Artificial Intelligence and Machine Learning in Veterinary Population Health

Department:

Veterinary Medicine | Veterinary Preventive Medicine

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.

Additional Information:

Assistant/Associate Professor of Artificial Intelligence and Machine Learning in Veterinary Population Health

Department of Veterinary Preventive Medicine

Colleg e of Veterinary Medicine

Position Overview

The Department of Veterinary Preventive Medicine in the College of Veterinary Medicine at The Ohio State University invites applications for a full-tim e , tenure-track faculty appo i ntme n t at the rank of Assistant or Associate Professor, specializing in Artificial Intelligence (AI) and Machine Learning (ML). We seek an AI/ML scholar whose research focuses on developing and applying innovative computational methods to address critical challenges in veterinary medicine , disease prevention and control , and population health . Ideal candidates will have a strong background in AI/ML and a demonstrated ability to translate advanced analytical approaches into meaningful discoveries and practical applications relevant to animal health and welfare. This position complements The Ohio State U niversity ’s AI H iring I nitiative to harness AI to revolutionize the translation of ideas to real-world solutions for Ohio and beyond.

The primary responsibilities of this position will be to conduct innovative, translational research at the intersection of advanced computational data science and veterinary medicine . Candidates must demonstrate significant AI/ML expertise as a core element of their scholarship through the development of novel methods or the application of existing approaches that generate new scientific knowledge, methodological advances , or practice solutions for veterinary preventive me dicine and population health . The s uccessful candidates will maintain an extramurally funded research program that prioritizes translational science discoveries using novel approaches that support animal health and/or welfare . The position is intended for faculty whose scholarly contributions are centered on AI/ML and their advancement or innovative application in vete ri nary and population heal th contexts, rather than the routine use of exisiting AI tools.

In addition to research, the successful candidate is expected to maintain a teaching program. T eaching responsibilities will include providing instruction to veterinary and graduate students on topics that may include preventive medicine, animal welfare, and/or disease diagnosis and prevention and control . The candidate may also be expected to develop and deliver content that expands AI literacy and promotes the responsible and effective use of AI tools in veterinary medic i ne and related disciplines.

The successful candidate is expected to serve in department, college, and university level initiatives including the OSU AI (x) Hub

that aims to pioneer AI innovation for the public good. They will also have the opportunity to engage with regulatory agencies and external stakeholders to use AI to advance veterinary medicine. The specific distribution of effort will be adjusted to reflect the strengths and interests of the successful candidate but will remain predominately focused on research.

Performance Objectives

Conduct translational research using AI/ML approaches to address challenges in veterinary medicine, disease prevention and control , and population health. Illustrative examples include:

Traditional and syndromic disease surveillance

Precision livestock farming , herd health informatics , and integrat ed decision support

Predictive modeling for disease prevention, diagnos is , treatment , and population health and welfare management

Population genomics, genetics and epidemiological forecasting

Develop and maintain a nationally and internationally recognized, extramurally funded research program focused on applied AI and machine learning in preventive veterinary medicine and population health.

Collaborate across disciplines (e.g., computer science, data analytics, public health, agriculture, medicine, and veterinary medicine) to foste r cross-cutting research .

Deliver high-quality instruction in core and elective topics, which may include veterinary preventive medicine, epidemiology, animal welfare, and data-drive n disease prevention and control

Contribute to educational activities that expand AI literacy and promote the responsible use and evaluation of AI in veterinary medicine

M entor graduate students (MS/PhD) and postdoctoral fellows in applied data science and population health

Engage with profession organizations, regulatory agencies, and indu stry stakeholders to advance the field of veterinary medicin e

Provide service to the department, college, and university through committee participation, academic governance, and participation in OSU AI(X) Hub.

Education and Experience Requirements

Ideal candidates will demonstrate significant AI expertise as a core element of their scholarship whether through the creation of new AI methods or novel use of AI techniques that materially advance knowledge, methods, or practice in their discipline.

For this search, “AI” includes (but is not limited to) Machine Learning (ML), Deep Learning, Reinforcement Learning, Optimization for/with ML, Probabilistic Modeling, Trustworthy/responsible AI (fairness, safety, privacy, robustness, interpretability), Human-centered AI, Natural Language Processing (NLP), Computer Vision, Robotics, Scientific/Physics-informed Machine Learning (ML), AI Systems and Infrastructure, Domain-driven AI advancements in and across disciplines.

In addition to the disciplinary requirements for pot