Postdoctoral Fellow - Department of Computer Science and Electrical Engineering

Description

The Lane Department of Computer Science and Electrical Engineering in the Benjamin M. Statler College of Engineering and Mineral Resources at West Virginia University invites applications for a Postdoctoral Fellow in the general areas of artificial intelligence, machine learning, and image processing. This position supports research initiatives related to automated target recognition in terrestrial and space-based satellite imagery, focusing on multisensor fusion, captioning and description of scene contents, and super-resolution.

Responsibilities will include but are not limited to the following : conducting research on target recognition; attending research conferences and read scientific literature to keep abreast of technological advances and current research findings; identifying and assisting with acquisition of funding from public, private, and governmental sources; searching scientific literature to select and modify methods and procedures most appropriate for research goals; performing laboratory and field experiment activities according to protocol; writing and presenting research / study status reports for review; preparing results of research findings for presentation to colleagues, and the sponsor at professional / technical conferences; work closely with graduate / undergraduate students, faculty members and industry partners to coordinate the project progress, contribute towards top quality technical journal publications, as directed.

West Virginia University is the comprehensive Land Grant University of the State of West Virginia, and a Carnegie Classification of Highest Research Activity (R1 University). The community lies within a high technology corridor that also includes several federal research facilities, such as the U.S. Department of Energy's National Energy Technology Laboratory, NASA's Independent Verification and Validation Facility (IV&V), the National Institute of Occupational Safety and Health (NIOSH) and the FBI, as well as private industry. The city is located within reasonable driving distances from Pittsburgh, PA and Washington, DC. Additional details on the area and the university are available at http : / / www.morgantownwv.gov / and http : / / www.wvu.edu .

Qualifications

A PhD in electrical engineering, computer science or related discipline and expertise in artificial intelligence and machine learning are required at the time of appointment. Applicants must also have expertise in the following areas : generative AI, pytorch / tesnorflow, and Linux operating systems.

To apply for this position, visit https : / / careers.wvu.edu / career-opportunities . Applicants must submit a cover letter, curriculum vitae, and contact information for two professional references. Only candidates who submit complete documentation will be considered in the review process. For questions or additional information, contact Prof. Jeremy M. Dawson, (phone : (304) 293-9687and email :

jeremy.dawson@mail.wvu.edu ). You may also visit the LCSEE website at https : / / lcsee.statler.wvu.edu .

West Virginia University is a place of purpose and community. We take pride in our profound impact on the state of West Virginia and are committed to the personal and professional growth of our employees.

From the groundbreaking R1 research at our flagship campus in Morgantown to the career-oriented programs at WVU Potomac State in Keyser, and the technology-intensive programs at WVU Tech in Beckley, the contributions of WVU employees resonate across the state, touching lives and shaping futures.

At WVU, you will discover a supportive community that champions work-life balance and fosters a collaborative atmosphere. Our core values - service, curiosity, respect, accountability and appreciation - unite us as Mountaineers.

Join us at West Virginia University, where your work will make a lasting impact. To learn more about WVU, visit wvu.edu .

West Virginia University is an Equal Opportunity Employer. We invite all qualified applicants regardless of race, color, religion, sex, national origin, age, disability, genetic information, or Veteran status.