Description:
Requested Minimum Qualifications
The ideal candidate for the position is expected to have the following minimum qualifications:
- Earned doctoral degree in engineering or closely related field;
- Evidence of ability to develop/update and teach undergraduate and graduate transportation systems courses in the areas of traffic flow theory, multimodal transportation planning and modeling, railway engineering, airport and seaport design and management, quantitative methods, and/or machine learning and big data analytics;
- Evidence of ability to secure grants and conduct/supervise high-level research in connected and autonomous vehicles, smart and connected infrastructure, alternative energy, artificial intelligence, autonomous systems, and/or learning-based control;
- Evidence of ability to work in a diverse and cross-disciplinary work environment; and
- Evidence of eligibility to work in the United States.
Post doc, industry, and/or prior teaching experience, and willingness to mentor students for the FE certification and ITE/ITS/ASHE student chapter activities is a plus.
Other Preferences for Consideration
The successful candidate should be prepared to:
- Teach undergraduate and graduate courses in transportation systems engineering.
- Contribute to research and scholarship opportunities that will enhance the research efforts of the Department and the Center for Equitable Artificial Intelligence and; Machine Learning.
- Supervise theses and dissertations
- Provide services to the department, college, university, community, and profession.
- Split their time evenly between the Center for Equitable Artificial Intelligence and; Machine Learning and the Department, with a 50% course load reduction.
- Demonstrate excellence in teaching.