University of Sussex · Brighton, East Sussex, BN1 9RH, United Kingdom, United Kingdom
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.
About the Role
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.
What You Will Do
Develop AI-driven ultrasonic methods for quantitative materials evaluation, focusing on inverse models that extract material properties or defect information from measurements.
Run large-scale ultrasound simulations to generate rich training datasets.
Design and benchmark modern neural network architectures.
Quantify the uncertainty of model predictions.
Validate your models in our ultrasonic laboratory to bridge the simulation-to-experiment gap.
Work across application areas including metals and layered structures.
Gain interdisciplinary skills spanning ultrasonic physics, numerical simulation, experimental measurement, and state-of-the-art AI.
Contribute to publications, conferences, and industry collaborations.
What We Are Looking For
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.
Experience in ultrasound or wave physics (preferred but not essential).
Experience in numerical simulation (preferred but not essential).
Python programming skills (preferred but not essential).
Machine learning frameworks experience (preferred but not essential).
Enjoyment of learning across disciplines and a creative mindset.
Commitment to making real research impact.
Benefits
Full funding covering tuition fees and a tax-free stipend (£21,805 per year).
World-class supervision and training in an interdisciplinary, research-active environment.
Opportunities to publish in leading journals and attend international conferences.
Collaboration with industry partners.
Vibrant student community at the University of Sussex, located near the beautiful coastal city of Brighton.
One-off Research and Training Support Grant of £2,000.
For informal enquiries, please contact Dr Ming Huang or Dr Ivor Simpson. This is a re-advertisement of an earlier position; previous applicants need not apply. Apply now to join a pioneering research team and shape the future of AI in materials science.