Postdoctoral Researcher - Department Of Civil And Environmental Engineering

The Wadsworth Department of Civil and Environmental Engineering at West Virginia University (WVU) invites applications for a Postdoctoral Researcher to work with Dr. Onur Avci on interdisciplinary research involving artificial intelligence, machine learning, computer vision, structural health monitoring, and condition assessment of civil infrastructure. The successful candidate will contribute primarily to externally funded research projects focused on applying advanced sensing, computational, and AI-based methods to infrastructure monitoring and decision-making. The projects focus on the development of automated methods for detecting and characterizing anomalies at mine-land and impoundment facilities; and identifying drainage conditions that contribute to pavement deterioration and developing data-driven methods for early detection and intervention prioritization. Research activities include the analysis of UAV-acquired imagery, LiDAR and 3D point-cloud data, computer-vision-based anomaly detection, machine learning, transfer learning, automated condition assessment; pavement condition and field-monitoring data; moisture measurements; computer vision; multimodal data integration; and development of infrastructure risk and decision-support tools.

Anticipated Start Date: January 2027

Appointment: Full-time, one-year appointment with potential for renewal based on performance and availability of funding

Responsibilities

The successful candidate will be expected to:

• Develop and implement machine-learning, deep-learning, and computer-vision methods for civil infrastructure applications.
• Process and analyze imagery, LiDAR/point-cloud, sensor, field, and infrastructure-condition datasets.
• Develop computational workflows for infrastructure anomaly detection, classification, condition assessment, and risk evaluation.
• Participate in field-data collection, experimental planning, and validation of developed methods.
• Integrate multiple data sources to support infrastructure monitoring and decision-making.
• Prepare technical reports, peer-reviewed journal manuscripts, conference papers, and presentations.
• Contribute to research proposal development and project deliverables.
• Collaborate with faculty, graduate students, government agencies, and other project partners.
• Assist in mentoring graduate and undergraduate researchers.

Required Qualifications

Applicants must have:

• A Ph.D. in Civil Engineering completed by the anticipated start date.
• Strong programming skills in Python.
• Experience with machine learning and/or deep learning.
• Experience with computer vision and image/data analysis.
• Experience with computational analysis and scientific programming.
• Strong written and oral communication skills.
• Ability to conduct independent research while working effectively within a multidisciplinary research team.

Preferred Qualifications

• At least three lead-author peer-reviewed publications.
• Research experience applying AI/ML to civil infrastructure, structural health monitoring, or infrastructure condition assessment.
• Experience with UAV-based inspection, RGB or thermal imagery, LiDAR, 3D point clouds, or other remote-sensing data.
• Experience developing deep-learning models for image classification, object detection, semantic segmentation, anomaly detection, or related applications.
• Experience working with large or heterogeneous engineering datasets.
• Experience with transportation infrastructure, pavement engineering, dams, embankments, mine-land infrastructure, or related systems.
• Demonstrated ability to prepare high-quality journal manuscripts and technical reports.

Application Materials

Interested applicants should submit:

• Curriculum vitae
• Cover letter describing research experience and interest in the position
• Brief research statement
• Copies or links to selected publications
• Contact information for three references

Applications should be submitted to https://careers.wvu.edu/career-opportunities.

Review of applications will begin November 9, 2026, and continue until the position is filled.