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 one of the most widely used techniques for non-destructive evaluation (NDE) and materials characterisation, underpinning safety-critical inspections in aerospace, energy, transport, and advanced manufacturing. Its ability to probe the internal state of materials, revealing microstructural features, defects, and degradation, makes it indispensable for both quality assurance and structural health monitoring.

However, extracting quantitative information from ultrasonic measurements remains a long-standing challenge. Forward modelling of wave propagation in complex defective media is computationally expensive, and the corresponding inverse problem of inferring material properties or defect characteristics from measured signals is typically ill-posed, non-linear, and highly sensitive to noise.

Recent advances in artificial intelligence offer a promising pathway to overcome these barriers. Deep learning models can act as ultra-fast surrogates for physics-based simulations, enabling near-instantaneous prediction of ultrasonic responses. They can also provide powerful inverse mapping capabilities that directly link measurements to underlying material states.

This PhD project will develop AI-driven ultrasonic methods for materials evaluation, combining surrogate modelling and inverse characterisation. The student will work across the full research pipeline, including building high-fidelity ultrasound simulations to generate rich training datasets, designing and benchmarking machine learning architectures, and validating the developed models against experimental measurements. Application targets will include metals, layered and composite structures, and additively manufactured components.

The successful candidate will join a vibrant and growing research centre at the University of Sussex, with access to state-of-the-art ultrasonic instrumentation, high-performance computing resources, and an active network of academic and industrial collaborators. The student will be trained in advanced ultrasonic theory, large-scale numerical simulation, experimental NDE, and modern AI techniques, gaining a highly interdisciplinary skill set that is 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 ML frameworks. Above all, we are looking for a curious, independent, and collaborative researcher who is eager to work at the interface of physics-based modelling, experiment, and AI.

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 Informatics [Starting in September 2026] 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 or your eligibility, contact FoSEM-PGR@sussex.ac.uk

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

Timetable

Application Deadline: 08 June 2026

Interview Date: 18 June 2026

Entry Date: September 2026