Assistant Professor-Data Science

1 week ago

Knoxville, United States University of Tennessee Full time

Assistant Professor of Data ScienceThe School of Natural Resources (SNR) at the University of Tennessee Institute of Agriculture in Knoxville invites applications for a 9-month, tenure-track position as an Assistant Professor of Data Science, 75% Research/25% Teaching. The successful candidate also will be a member of the and . The individual must be committed to the Land Grant University mission of teaching, research, and outreach, with a strong interest in mentoring undergraduate and graduate students. The finalist’s expertise should complement the School’s existing research and teaching programs. The successful candidate will develop a research and teaching program focused on educating applied data scientists, with an emphasis on natural resource processing and manufacturing. We welcome applicants with experience in any of the following: data quality, data fusion, supply chain management, design of experiments, statistical process control, artificial intelligence, machine learning, process modelling, life cycle assessment and technoeconomic analysis. Applicants with experience in data science applications to bio-based energy and materials are encouraged to apply. Applicants should have demonstrated excellent verbal and written communication skills, and the ability and willingness to work in an interdisciplinary environment. Teaching responsibilities will include developing and teaching an introductory undergraduate class in data science for natural resources and a graduate class in data science focused on the applicant’s expertise. The successful candidate will participate in the new and the ongoing undergraduate and graduate SNR programs. The finalist will have the opportunity to leverage existing relationships with the nearby and the . Potential collaborative groups at the University of Tennessee include the , the , the , and the . The successful candidate will be encouraged to seek collaboration with private industry, non-governmental organizations, and other state and federal agencies. In addition to securing extramural funding, the successful candidate will be expected to publish in peer-refereed journals, advise undergraduate students, direct graduate students in research, serve on School committees, and participate in professional societies. Required qualifications: A . in data science, statistics, industrial engineering, biomaterials science, forestry, or a related field, completed before the position start date. Desired qualifications: Experience in a processing and/or manufacturing industry; a strong publication record; success in grant writing; supervisory experience, understanding of the Land Grant missions; and a good record of collaboration and cooperative research with state and federal natural resource agencies, non-government organizations, and industry.

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