"Research Assistant Professor of Software Engineering & Optimization - School of Engineering, Architecture and Aviation"

The Hampton University School of Engineering, Architecture and Aviation is seeking a candidate for appointment in the Department of Electrical and Computer Engineering as a Research Assistant Professor of Software Engineering & Optimization. The position will be filled immediately or will remain open until filled. This position is located in Hampton, Virginia.

Founded in 1868, Hampton University (www.hamptonu.edu) is located on approximately 300 acres of the Virginia Peninsula. It is a privately endowed, co-educational, nonsectarian institution of higher education and is fully accredited by the Southern Association of Colleges and Schools (SACS). Hampton University, an Historically Black College and University (HBCU), is enjoying tremendous growth and development from a traditionally teaching to a more research-intensive institution. Hampton University is currently an R2 Carnegie classification institution of higher learning with a primarily undergraduate student population of approximately 4,200 students pursuing degrees in diverse areas of study.

It is pertinent to note that Hampton University is a consortium member of the Research Institute for Tactical Autonomy (RITA), which is a University Affiliated Research Center (UARC) funded by the Department of Defense/Department of the Air Force. The selected candidate will conduct research for the RITA UARC and must be a US Citizen.

Duties and Responsibilities

Hampton University is seeking a candidate for an appointment at the Research Assistant Professor of Software Engineering & Optimization. The individual selected for this position will be expected to contribute to the research missions of the Departments of Electrical & Computer Engineering and the RITA UARC by conducting research related to Autonomy, Artificial Intelligence and Machine Learning. The selected candidate will be responsible for supervising undergraduate students and working with the Engineering and Aviation faculty to complete research tasks assigned by the RITA UARC.

Qualifications

The successful applicant must have a master's degree or doctorate degree in either Computer Engineering, Computer Science, Electrical Engineering, or a closely related field with expertise in one or more of the following areas: machine learning, reinforcement learning, artificial intelligence, optimal control theory, optimization, cognitive science, social science, tactical autonomy, and/or cloud-based computing.

The successful candidate will possess the following skills and abilities:

• An established record of research: The candidate should have a strong track record of publishing high-quality research papers in top-tier journals and conferences.
• A deep understanding of AI/ML: The candidate should have a deep understanding of the theoretical foundations of AI/ML, as well as the latest advances in these fields. They should also be familiar with a variety of AI techniques.
• A proven ability to mentor students: The candidate should have a proven ability to mentor and train undergraduate researchers. They should be able to create a supportive learning environment that encourages students to reach their full potential.
• Understanding algorithm development and optimization (genetic algorithms LP/SQP) techniques.
• State machine learning (a plus)

How to Apply

Review of applications will begin immediately and will continue until the position is filled. The compensation package will be commensurate with experience. All interested applicants should submit a cover letter, curriculum vitae, research statement and a completed Hampton University Application for Faculty Employment form electronically (only) to joyce.shirazi@hamptonu.edu:

Dr. Joyce Shirazi
Dean, School of Engineering, Architecture and Aviation
Hampton University
Hampton, VA 23668
joyce.shirazi@hamptonu.edu

Forms
Visit https://home.hamptonu.edu/hr/employment/ to retrieve the faculty employment application and other supplemental application materials.