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Machine Learning / AI for Innovation in Ultrasonic Transducers for Underwater Sonar


   FUSE CDT

  Prof Sandy Cochran  Applications accepted all year round  Funded PhD Project (European/UK Students Only)

About the Project

We are continuing to welcome applications at this time.

Industrial Partner: Thales

Underwater sonar is the most important imaging technique for use underwater, finding applications in marine surveying, ocean monitoring, shipping navigation, and the defence and fishing industries, amongst others.

The vast majority of sonar systems rely on transducers made with piezoelectric materials to generate and detect ultrasound signals to interrogate and gain knowledge of the marine environment.  

Innovation in ultrasonic transducer materials and structures has underpinned many of the performance improvements that have been achieved in the past 100 years.

Examples in structures include: flextensional devices from the 1970s onwards; capacitive micromachined ultrasonic transducers (CMUTs) developed since the 1990s; their piezoelectric equivalent, PMUTs, developed more recently; and structures tailored to exploit d36 properties in piezocrystals.

However, the potential for innovation is often seen as in tension with established use of existing transducers, partly because of the long lifetime of such devices, and it therefore usually takes decades for the potential to be realised.

This PhD project, sponsored by Thales UK, will suit an engineer or computer scientist with a particular interest in applications of AI. It will study how innovation based on AI can be characterised, understood and codified, with the aim to shorten the time to realise the potential of innovative structures and materials.

This will be done through exploration of the use of machine learning and AI in the design of modified and entirely new transducers, exploiting the power of virtual prototyping across a range of software platforms implementing finite element analysis and simpler tools.

The results will be documented technically and with regard to the procedures developed, including interaction with sonar transducer designers with different skill levels.

Funding Notes

Funding is available to cover tuition fees for UK Home applicants and a stipend at the Research Council rate (£16,062 for 2022/23). To be eligible for a fully-funded Home studentship, you must be:

  • a UK citizen (who has been resident in the UK/EEA/Switzerland/Gibraltar for the past three years)
  • an EU citizen with ‘settled’ or ‘pre-settled’ status in the UK.
  • an applicant with ‘indefinite leave to remain or enter’ the UK

We also accept international applicants that can self-fund or bring external funding to cover their tuition and living costs.

How to Apply

Applications must be submitted through the University of Glasgow online application system with a cover letter, CV, two references and your transcript/degree certificate. When applying to this project, please insert the project name in to the “proposed thesis title” section and add “Prof Sandy Cochran” as a placeholder supervisor for the application.

Please visit our website for further details about the FUSE CDT and how to apply.

For enquiries, please email

Keywords: Glasgow / United Kingdom / Machine Learning / Physics - Other / Electrical Engineering / Software Engineering / Artificial Intelligence / Marine Engineering


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