AI-Driven Ultrasound for Materials Evaluation

AI-Driven Ultrasound for Materials Evaluation AI-Driven Ultrasound for Materials Evaluation (2026) In this PhD, you will help develop AI-driven ultrasonic methods for materials evaluation. Depending on your interests, you may focus on building fast AI surrogate models that replace expensive simulations, on inverse models that directly extract material properties or defect information from measurements, or on both. You will work across simulation, experiment, and machine learning, including running large-scale ultrasound simulations, designing modern neural network architectures, and validating your models in our ultrasonic laboratory. What you get For 3.5 years, you will receive a tax-free stipend at a standard rate of £21,805 per year and your fees will be waived (at the UK or International rate). In addition, to a one-off Research and Training Support Grant of £2,000. Type of award Postgraduate Research PhD project Ultrasound is a widely used technique for non-destructive evaluation (NDE) of materials. Its ability to probe the internal state of materials makes it indispensable for revealing microstructural features, defects, and degradation, thereby underpinning safety-critical inspections in energy, transport, and advanced manufacturing. However, extracting quantitative information from ultrasonic measurements remains a long-standing challenge. Wave propagation in complex media is difficult to model. The inverse problem of inferring material properties or defect characteristics from measurements is typically ill-posed, non-linear, and highly sensitive to noise. Recent advances in artificial intelligence offer a promising route past these barriers. Deep learning models, trained on physics-based simulations and complemented by limited experimental data, can learn the mapping from ultrasonic responses to material states, enabling quantitative inference once trained. This PhD project will develop AI-driven ultrasonic methods for quantitative materials evaluation. The student will work across the full research pipeline. This includes building high-fidelity ultrasound simulations to generate rich training datasets, designing and benchmarking machine learning architectures, quantifying the uncertainty of model predictions, and validating the developed models against experimental measurements to bridge the simulation-to-experiment gap. Applications will target metals and layered structures. The successful candidate will join a vibrant and growing research centre at the University of Sussex. They will have access to state-of-the-art ultrasonic instrumentation, high-performance computing resources, and an active network of academic and industrial collaborators. Training will include advanced ultrasonic theory, large-scale numerical simulation, experimental NDE, and modern AI techniques, providing a highly interdisciplinary skill set in strong demand across academia and industry. We welcome applications from highly motivated candidates with a strong back