PhD Studentship: AI-Driven Ultrasound for Materials Evaluation (2026)

Would you like to use cutting-edge artificial intelligence to solve real-world physics and engineering problems that matter? This fully funded PhD studentship offers an exceptional opportunity to do exactly that, working at the forefront of AI-driven ultrasonic evaluation of materials. Ultrasound plays a critical role in keeping our world safe and reliable. From inspecting aircraft components and pipelines to monitoring batteries in electric vehicles, ultrasonic measurements let us “see” inside materials without...

Opportunity details

Would you like to use cutting-edge artificial intelligence to solve real-world physics and engineering problems that matter? This fully funded PhD studentship offers an exceptional opportunity to do exactly that, working at the forefront of AI-driven ultrasonic evaluation of materials. Ultrasound plays a critical role in keeping our world safe and reliable. From inspecting aircraft components and pipelines to monitoring batteries in electric vehicles, ultrasonic measurements let us “see” inside materials without damaging them. Yet interpreting the resulting signals is far from straightforward: waves in real materials behave in complex ways, and turning measurements back into quantitative information about a material’s internal state is a notoriously difficult inverse problem. This is where AI can be a game changer. Deep learning models can learn the mapping between material states and ultrasonic responses from simulation data, delivering quantitative predictions once trained, and remarkably, can be transferred to work directly on experimental measurements. In this PhD, you will develop AI-driven ultrasonic methods for quantitative materials evaluation, focusing on inverse models that extract material properties or defect information from measurements. You will work across the full research pipeline: running large-scale ultrasound simulations to generate rich training datasets, designing and benchmarking modern neural network architectures, quantifying the uncertainty of model predictions, and validating your models in our ultrasonic laboratory to bridge the simulation-to-experiment gap. Application areas include metals and layered structures. You will join a dynamic and rapidly growing research centre at Sussex, with access to advanced ultrasonic instrumentation, hig...

Eligibility

What we are looking for: We welcome applications from enthusiastic, curious, and self-motivated candidates with (or expecting) a strong undergraduate or Master's degree in physics, engineering, applied mathematics, materials science, computer science, or a related discipline. Prior experience in any of the following is a plus but not essential: ultrasound or wave physics, numerical simulation, Python programming, and machine learning frameworks. What you will get: A fully funded studentship covering tuition fees and a stipend (open to UK and international applicants). Please note that this is a re-advertisement of an earlier position; previous applicants need not apply.

Requirements

What we are looking for: We welcome applications from enthusiastic, curious, and self-motivated candidates with (or expecting) a strong undergraduate or Master's degree in physics, engineering, applied mathematics, materials science, computer science, or a related discipline. Prior experience in any of the following is a plus but not essential: ultrasound or wave physics, numerical simulation, Python programming, and machine learning frameworks. What you will get: A fully funded studentship covering tuition fees and a stipend (open to UK and international applicants). Please note that this is a re-advertisement of an earlier position; previous applicants need not apply.

Funding and benefits

Funding eligibility: UK Students, International Students. Funding amount: 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..

How to apply

Application areas include metals and layered structures. What we are looking for: We welcome applications from enthusiastic, curious, and self-motivated candidates with (or expecting) a strong undergraduate or Master's degree in physics, engineering, applied mathematics, materials science, computer science, or a related discipline. 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.

Open the official application information

Verify the deadline and final conditions on the official provider page before submitting an application.