This project aims to leverage the advances in ultrasonic testing, including optical fibre-based distributed acoustic sensing and physics-enhanced machine learning, to increase the reliability of nuclear waste storage monitoring.
The security of energy generation and management facilities is essential to the defence strategy. These assets often involve extreme environmental conditions, either due to high pressure, high temperature or ionising radiation. Nuclear waste storage is one significant example where the integrity of containers over very long periods requires reliable monitoring. However, this task is a formidable challenge, given the risky conditions and the scale of the structures involved.
This project aims to leverage the advances in ultrasonic testing, with the recent growth of optical fibre-based distributed acoustic sensing technology and physics-enhanced machine learning, to deliver solutions for reliable long-range monitoring. Both sensing modalities have limitations, and one of the project aims is to determine their practical significance and develop techniques to overcome them using modern computing. Both active (with purposely induced acoustic signals) and passive (e.g. using leak noise, ambient vibration, intrusion, seismic activity, etc.) methods are of interest, together with a framework using the available capability most optimally. The student will engage in numerical and analytical modelling, developing machine learning frameworks for metamodelling and decision support, as well as experimental tests on mock-up configurations.
For informal enquiries please get in touch with the Dr Michal Kalkowski – M.Kalkowski@soton.ac.uk
Funding for this project is offered by the Centre for Doctoral Training in Complex Integrated Systems for Defence & Security (CISDnS), which will recruit motivated and inquisitive candidates across the themes of Digital, Physical and Biological systems to provide a diverse and interconnected cohort training environment. You can read more about the Centre and the training programme at https://cisdns-cdt.ac.uk/
To discuss aspect related to the CISDnS CDT please contact the directorate – cisdns@soton.ac.uk
This PhD studentship is open only to UK applicants. This project is suitable for applicants with background in mechanical engineering, acoustics, numerical modelling, scientific computing, signal processing, machine learning, or applied mathematics.
CISDnS is committed to promoting equality, diversity and inclusivity. We welcome all applicants regardless of their gender, ethnicity, disability, sexual orientation or age, and will give full consideration to applicants seeking flexible working patterns and those who have taken a career break or are transitioning into a new role. The University has a generous maternity policy, onsite childcare facilities, and offers a range of benefits to help ensure employees’ well-being and work-life balance.
Entry requirements: A very good undergraduate degree (at least a UK 2:1 honours degree, or its international equivalent). Following the diversity objectives of CISDnS, we will accept many forms of equivalent prior learning.
Funding: Full-time studentships will cover UK tuition fees and an enhanced tax-free stipend of approx. £24,700 per year for 4 years, along with a substantial budget for research, travel, and centre activities.
How to apply
Search for a Postgraduate Programme of Study (soton.ac.uk).
Select Full-time or Part-time, programme type (Research), 2025/26, Faculty of Engineering and Physical Sciences, next page select “iPhD Complex Integrated Systems in Defence & Security (Full-Time)”
In Section 2 of the application form you should insert the name of the supervisor Dr Michal Kalkowski.
Applications should include:
- curriculum vitae giving details of your academic record and stating your research interests
- name two current academic referees together with an institutional email addresses in the Reference section of the application form. On submission of your online application your referees will be automatically emailed requesting they send a reference to us directly by email.
- your academic transcript and degree certificate (translated if not in English) - if you have completed both a BSc & an MSc, we require both.
- include a short statement of your research interests in the Personal Statement section of the application form.
