Giulia Marasco

A PhD position is available in the Department of Civil and Environmental Engineering at Florida International University, supervised by Assistant Professor Giulia Marasco. The research focuses on the intersection of artificial intelligence, automation, and bridge engineering, with two main areas: AI-based bridge assessment (including intelligent monitoring systems, digital twins, sensor integration, and predictive maintenance) and AI-assisted additive manufacturing for bridge automation (exploring 3D printing, AI-based form finding, and advanced optimization techniques). The ideal candidate will have a Master’s degree in Civil Engineering or a related field, strong programming skills (Python, MATLAB, or C++), analytical abilities, motivation, and good communication skills. The position is suited for students passionate about AI, automation, and infrastructure. Applications are accepted on a rolling basis for a Spring 2026 start. To apply, send a CV and a 1-page research statement to the supervisor. Funding details are not specified in the announcement.

Florida International University (FIU) is offering a PhD position in the area of AI-Enhanced Bridge Digital Twins and Structural Health Monitoring, starting Fall 2026. The position is supervised by Assistant Professor Giulia Marasco, who is also the Co-Director of the Accelerated Bridge Construction University Transportation Center (ABC-UTC). The research project aims to advance next-generation bridge assessment by developing intelligent monitoring systems and high-fidelity digital twins. The work will integrate cutting-edge sensing technologies, such as wireless sensor networks, mobile sensing platforms (including vehicle- or UAV-based sensors), and acoustic emission systems, to enable dense, scalable, and real-time data acquisition for structural health monitoring. The central focus will be on data-driven and physics-informed modeling approaches for damage detection, condition assessment, and prognosis. Advanced deep learning techniques, including graph neural networks, spatio-temporal models, and hybrid AI-physics frameworks, will be explored to capture the complex structural topology of bridges, sensor interdependencies, and multi-scale degradation mechanisms. The models will be designed to fuse heterogeneous data sources and embed physical constraints, improving interpretability, robustness, and generalization. The ultimate goal is to develop AI-powered diagnostic and predictive tools that enhance bridge digital twins, enabling continuous condition monitoring, early damage detection, and reliable predictive maintenance strategies to support data-driven decision-making in infrastructure management. Applicants should have a Master's degree in civil engineering or a related field, programming skills (Python, MATLAB, or C++), a strong analytical mindset, self-motivation, and good communication skills. Interested candidates should send their CV and a 1-page research statement to [email protected]. For more details, refer to the attached document and the supervisor's LinkedIn profile.