[School of Natural Sciences PhD Scholarships] Machine Learning-Enabled Ultrasonic and Thermal Inspection for Quantitative Composite Defect Mapping

About the project

About the Project Carbon Fiber Reinforced Polymers (CFRP) and advanced composite laminates are critical to modern aerospace structures, automotive components, and renewable energy infrastructure. Ensuring structural integrity requires rapid, reliable Non-Destructive Testing (NDT) to identify manufacturing defects such as micro-porosity, delaminations, and foreign inclusions. However, conventional NDT screening relies heavily on visual inspection of 2D images, which can be time-consuming and subjective. This PhD project aims to revolutionize composite inspection by developing an automated, operator-independent AI framework that combines high-resolution Immersion Ultrasonic Testing (Immersion UT) and Active Infrared Thermography (Active IRT). By pairing multi-modal acoustic and thermal wavefield datasets with benchmarked 3D X-ray Computed Tomography (XCT) ground truth, you will train state-of-the-art neural networks (CNNs, Physics-Informed Neural Networks) to automatically detect, classify, and quantitatively measure defect volumes and porosity levels within composite structures. Key research tasks include: - Manufacturing composite test panels with controlled stochastic porosity and precision micro-defects. - Conducting immersion ultrasonic scans and active thermographic inspections to collect high-fidelity signal data. - Processing raw RF waveforms and thermal transient maps to extract key physical features (attenuation, effusivity, dispersion). - Developing and evaluating deep learning models for automated defect recognition and void fraction prediction. As a PhD researcher on this project, you will gain multidisciplinary expertise spanning composite manufacturing, advanced experimental NDT, signal processing, and artificial intelligence. Based in the Department of Materials at The University of Manchester, you will work in state-of-the-art laboratories with access to world-class research infrastructure. This project provides an exceptional balance of hands-on experimental testing and cutting-edge computational modeling, preparing you for leadership roles in academic research, aerospace, or high-tech engineering industries. This project is expected to start in September 2027. Before you apply: We strongly recommend that you contact the supervisors for this project before you apply. How to apply: To be considered for this project you must complete a formal application through our online application portal. If you already have an applicant account this link will directly open an application for PhD School of Natural Sciences Scholarships . If you don’t already have an applicant account, please follow the instructions here . When applying, please specify the full title and supervisor/s of the project, details of your previous study, and names and contact details of two referees. You must also upload a Supporting Statement describing your motivation to apply to the project, your CV and transcripts of awarded and in-progress university qualifications . Please note late or incomplete applications will not be considered. Equality, diversity and inclusion are fundamental to the success of The University of Manchester and central to all our activities. A diverse research community strengthens creativity, productivity and quality, while increasing the societal and economic impact of our work. We welcome applicants from all career paths, backgrounds and sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation or transgender status. We welcome applications from candidates returning to study after a career break or experience in other roles. Flexible study arrangements may be available, including part-time study at 50%, 60% or 80%, subject to the requirements of the project and funder. Eligibility : The standard academic entry requirement for this PhD is an upper second-class (2:1) honours degree (or international equivalent) in Materials Science and Engineering, Mechanical Engineering, Aerospace Engineering, Physics or Computer Science OR any upper-second class (2:1) honours degree and a Master’s degree at merit (or international equivalent) in Materials Science and Engineering, Mechanical Engineering, Aerospace Engineering, Physics or Computer Science. Previous research experience or strong interest in non-destructive testing, ultrasonic/thermal testing, signal processing, composite materials, or machine learning (Python/PyTorch/MATLAB) is highly desirable. This project will remain open until filled. If your application is submitted by 1 st November 2026, you can expect a decision by 18 th December 2026. If your application is submitted by 15 th January 2027, you can expect a decision by 30 th March 2027. Self or externally funded students can also be considered for this project. FSESoNS

Research areas