Description
The College of Emerging and Collaborative Studies (CECS) at the University of Tennessee, Knoxville's (UTK) seeks a dynamic, collaborative, and innovative faculty member to contribute to its existing and future programs in Artificial Intelligence. CECS has one position open in Artificial Intelligence for a non-tenure track, nine-month, full-time appointment, on campus, beginning January 1, 2027. This is an open-rank search; appointment at the Teaching Assistant Professor, Teaching Associate Professor, or Teaching Professor level will be commensurate with qualifications and experience.
The selected candidate will be responsible for teaching and service, with assignments made by the dean according to enrollment demands and scheduling. Primary teaching responsibilities will include courses in Artificial Intelligence spanning introductory through graduate-level offerings, as well as other new courses launched by the College. We are seeking a colleague who brings deep applied expertise in one or more artificial intelligence domains and who shares our commitment to education that is hands-on, intercollegiate, and workforce-relevant. Candidates are expected to maintain a scholarship focused on practice and impact; traditional academic research is welcome but not required.
Expertise in the following teaching areas is expected:
Artificial Intelligence: The College is especially interested in candidates whose primary strength lies in generative AI and large language model engineering, agentic AI and multi-agent systems, AI systems evaluation and safety, or AI forward deployed engineering — including retrieval-augmented generation, LLM fine-tuning, agent orchestration and tool use, prompt engineering, monitoring and evaluation of production AI systems (robustness testing, red-teaming, and hallucination detection), and the applied, client-facing deployment of AI solutions in real-world settings — as these are current strategic priorities for the program. Beyond that focus, expertise is also expected in: introductory artificial intelligence concepts and applications; natural language processing and conversational AI; human-AI interaction and user experience design; AI-based data handling, preprocessing, and visualization; and programming in Python using frameworks such as Scikit-learn, TensorFlow, and PyTorch. The ideal candidate will bring the knowledge and skills to teach courses such as Agentic AI and Multi-Agent Systems, Generative AI and Large Language Model Engineering, AI Systems Evaluation and Safety, and AI Forward Deployed Engineering, should the program choose to offer them in the future.
Key Responsibilities
The College of Emerging and Collaborative Studies (CECS) at the University of Tennessee, Knoxville's (UTK) seeks a dynamic, collaborative, and innovative faculty member to contribute to its existing and future programs in Artificial Intelligence. CECS has one position open in Artificial Intelligence for a non-tenure track, nine-month, full-time appointment, on campus, beginning January 1, 2027. This is an open-rank search; appointment at the Teaching Assistant Professor, Teaching Associate Professor, or Teaching Professor level will be commensurate with qualifications and experience.
The selected candidate will be responsible for teaching and service, with assignments made by the dean according to enrollment demands and scheduling. Primary teaching responsibilities will include courses in Artificial Intelligence spanning introductory through graduate-level offerings, as well as other new courses launched by the College. We are seeking a colleague who brings deep applied expertise in one or more artificial intelligence domains and who shares our commitment to education that is hands-on, intercollegiate, and workforce-relevant. Candidates are expected to maintain a scholarship focused on practice and impact; traditional academic research is welcome but not required.
Expertise in the following teaching areas is expected:
Artificial Intelligence: The College is especially interested in candidates whose primary strength lies in generative AI and large language model engineering, agentic AI and multi-agent systems, AI systems evaluation and safety, or AI forward deployed engineering — including retrieval-augmented generation, LLM fine-tuning, agent orchestration and tool use, prompt engineering, monitoring and evaluation of production AI systems (robustness testing, red-teaming, and hallucination detection), and the applied, client-facing deployment of AI solutions in real-world settings — as these are current strategic priorities for the program. Beyond that focus, expertise is also expected in: introductory artificial intelligence concepts and applications; natural language processing and conversational AI; human-AI interaction and user experience design; AI-based data handling, preprocessing, and visualization; and programming in Python using frameworks such as Scikit-learn, TensorFlow, and PyTorch. The ideal candidate will bring the knowledge and skills to teach courses such as Agentic AI and Multi-Agent Systems, Generative AI and Large Language Model Engineering, AI Systems Evaluation and Safety, and AI Forward Deployed Engineering, should the program choose to offer them in the future.
Key Responsibilities
- Teach courses spanning introductory through graduate level in Artificial Intelligence, including lab-intensive and applied learning components