PhD Position in Structural Engineering, Timber Systems, and AI at University of North Carolina at Charlotte

The University of North Carolina at Charlotte is offering a fully funded PhD position in structural engineering, with a focus on resilient infrastructure, mass timber systems, and AI-driven structural reliability. The position is supervised by Dr. Aivars Vilguts, Assistant Professor and structural engineer, whose research integrates advanced structural analysis, probabilistic modeling, and machine learning to advance the reliability and robustness of timber and hybrid structural systems under uncertainty. The research vision centers on developing next-generation methods for evaluating, predicting, and enhancing the performance of wood and mass timber structures, especially in the context of climate-driven hazards such as hurricanes, floods, and windstorms. The project lies at the intersection of structural mechanics, nonlinear analysis, finite element methods, uncertainty quantification, and risk-informed decision-making for infrastructure. Candidates will work on topics such as damage propagation in structures, fragility curves for timber buildings and bridges, modeling structural behavior, using machine learning for performance prediction, and improving design methods for climate-resilient buildings. Applicants should have a strong background in structural or civil engineering, programming skills (Python, MATLAB, or similar), and experience in wood property testing and experimental analysis. Preferred qualifications include experience with mass timber structures, nonlinear finite element modeling, and an interest in sustainability and AI integration in engineering. The successful candidate will join a vibrant research program at UNC Charlotte, a Carnegie R1 research university with strong industry partnerships in infrastructure and advanced materials. The position offers a funded research assistantship (tuition plus competitive stipend) and will start in Fall 2026. The application deadline is March 13, 2026. To apply, send a single PDF with your CV, a 1-page statement of research interests, academic transcripts, and contact information for two references to Dr. Vilguts at [email protected]. For more information, see the LinkedIn announcement and Dr. Vilguts' academic profile.