AI-Driven Ultrasound for Materials Evaluation

AI-Driven Ultrasound for Materials Evaluation (2026)

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 background in physics, engineering, applied mathematics, materials science, or computer science. Experience in any of the following is desirable but not essential: ultrasound or wave physics, numerical simulation, Python programming, and AI models. Above all, we are looking for a curious, independent, and collaborative researcher eager to work at the interface of physics-based modelling, experiment, and AI.

Please note that this is a re-advertisement of an earlier position; previous applicants need not apply. For informal enquiries, please contact Dr Ming Huang or Dr Ivor Simpson.

Eligibility

This studentship is available to UK and Overseas applicants.

Eligible candidates will normally have an upper second-class (2:1) undergraduate honours degree (or equivalent qualification) in a related field.

The University of Sussex believes that the diversity of its staff and student community is fundamental to creative thinking, pedagogic innovation, intellectual challenge, and the interdisciplinary approach to research and learning. We celebrate and promote diversity, equality and inclusion amongst our staff and students. As such, we welcome applicants from all backgrounds.

Number of scholarships available

1

Deadline

How to apply

Apply online for a full time PhD in Engineering [Starting in January 2027] using our step-by-step guide.

Please ensure you application includes each of the following:

• A research proposal.
• Your CV.
• Degree certificates and transcripts.
• 2 references, including a minimum of 1 from any institution studied at within the last 5 years.
• If your first language is not English you will need to demonstrate that you meet the University’s English language requirements.

Please clearly state on your application that you are applying for the AI-driven ultrasound for materials evaluation studentship under the supervision of Dr Ming Huang.

Contact us

If you have practical questions about the progress of your on-line application, contact FoSEM-PGR@sussex.ac.uk

For academic questions please contact ming.huang@sussex.ac.uk

Timetable

Application Deadline: 11 September 2026

Interview Date: 24 September 2026

Entry Date: January 2027