Artificial intelligence for ultrasonic characterisation of complex materials University of Southampton in United Kingdom

Explore the space where physics-based modelling and artificial intelligence meet. Join this project on ultrasonic non-destructive characterisation of complex materials. This project will explore various artificial intelligence tools combined with ultrasonic experiments, physics-based simulation and inversion to develop reliable tools for safety-critical assets.

You will join the Dynamics Group within the University's Institute of Sound and Vibration Research . You will contribute to developing an exciting capability in AI-powered non-destructive evaluation (NDE), working alongside researchers who use acoustics to interrogate and characterise structures and materials of different scales and complexities.

We are a member of the UK's Research Centre for Non-Destructive Evaluation (RCNDE) and run projects in both ultrasonic and X-ray imaging ,including EU-funded iWeld .

This project tackles a long-standing challenge in ultrasonic non-destructive testing – characterisation and inspection of complex materials, such as additively manufactured components, castings, or thick weld sections.

These materials are safety-critical in various industries, such as nuclear energy. However, they are notoriously difficult to inspect because their complex microstructure deviates and scatters ultrasound. The currently dominant reliance on destructive examination is holding the industry back from reliable asset management.

This project aims to push the boundaries of characterisation, allowing for in-situ solutions accounting for likely uncertainties in sensor positions, geometry and alike. To achieve this, we will explore various artificial intelligence tools combined with experiments, physics-based simulation and inversion.

Co-supervisor: Prof. Thomas Blumensath

Entry requirements

A UK 2:1 honours degree, or its international equivalent .

Essential skills:

• numerical skills - coding
• scientific computing
• signal processing
• strong understanding of mechanics of materials

Desirable skills:

• acoustical engineering
• ultrasonics
• machine learning
• material examination

How to apply Artificial intelligence for ultrasonic characterisation of complex materials | University of Southampton

You need to:

• choose programme type (research), 2025/26, Faculty of Engineering and Physical Sciences
• please select if you will be full time or part time
• choose the relevant PhD in Engineering
• add name of the supervisor in section 2

Applications should include:

• a personal statement
• your CV (resumé)
• 2 academic references
• degree transcripts to date