Summary
This PhD studentship focuses on developing AI-driven ultrasonic methods for materials evaluation. The successful candidate will work at the intersection of simulation, experiment, and machine learning to extract quantitative information from ultrasonic measurements, leveraging recent advances in artificial intelligence.
PhD Studentship in AI-Driven Ultrasound for Materials Evaluation, University of Sussex, UK
Designation
PhD Studentship in AI-Driven Ultrasound for Materials Evaluation
Table
| Parameter | Details |
|---|---|
| Research Area | Ultrasonic methods for materials evaluation |
| Location | University of Sussex |
| Stipend | ยฃ21,805 per year (tax-free) |
| Duration | 3.5 years |
| Training Support Grant | ยฃ2,000 |
| Application Deadline | 8 June 2026 |
| Interview Date | 18 June 2026 |
| Entry Date | September 2026 |
Research Area
The project focuses on non-destructive evaluation (NDE) and materials characterization using ultrasound. It addresses challenges in extracting material properties and defect information from measurements using advanced AI techniques.
Location
University of Sussex
Eligibility/Qualification
- Degree Requirement: Upper second-class (2:1) undergraduate honors degree or equivalent in a related field (e.g., physics, engineering, applied mathematics, materials science, computer science).
- Desirable Skills: Experience in ultrasound/wave physics, numerical simulation, Python programming, and machine learning frameworks is an asset, but not essential.
- Personal Attributes: Curious, independent, and collaborative approach to research.
Job Description
The PhD candidate will:
- Develop fast AI surrogate models replacing expensive simulations.
- Conduct inverse modeling to extract material properties and defect information directly from measurements.
- Build high-fidelity ultrasound simulations for creating rich training datasets.
- Design and benchmark machine learning architectures, validating models against experimental data.
- Engage with academic and industrial collaborators, accessing state-of-the-art ultrasonic instrumentation and high-performance computing resources.
How to Apply
To apply for this PhD studentship, complete the online application for a full-time PhD in Informatics and include:
- A research proposal.
- Your CV.
- Degree certificates and transcripts.
- Two references (one must be from an institution studied at within the last 5 years).
- Proof of English language proficiency (if applicable).
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.
Last Date for Apply
8 June 2026, 23:45 (GMT)
For further inquiries related to the application process, contact: FoSEM-PGR@sussex.ac.uk. For academic questions, please reach out to: ming.huang@sussex.ac.uk.
