About the Project
Supervisory team: Professor Thomas Blumensath and Dr Richard Boardman
About the project
In this project you will develop advanced machine learning tools to convert real X-ray measurement data into artefact free 3D images. The work draws on a range of ideas from artificial intelligence, optimization, X-ray physics, applied mathematics and computer science.
Machine learning has revolutionised many scientific fields and has become an increasingly useful tools in a wide range of image processing applications; including in X-ray tomography. X-ray tomography (XCT) is an imaging technique that utilises X-ray radiation to generate a three-dimensional image of internal object structures. The technique is thus used routinely in medical diagnostics, security screening, industrial non-destructive testing as well as scientific investigations.
In many X-ray tomography applications, constraints on the imaging process mean that we are often only able to collect limited X-ray measurements, which can lead to significant image noise and artefacts. Many advanced machine learning methods have thus been proposed to reduce these errors. In this project, you will be exploring the use of the recently introduced deep equilibrium models, which are advanced artificial intelligence tools that combine ideas from optimisation, inverse problems and deep learning. Your work will focus on the use of these methods in realistic X-ray imaging settings with the goal to develop tools that can be readily applied to large real datasets.
To achieve these goals, several challenges need to be addressed, including the development of efficient computational methods to cope with realistically sized image data as well as the limits imposed by the lack of training data in many settings.
Whilst the project will be predominately computational, there will also be the chance to work closely with the University of Southampton’s dedicated X-ray Computed Tomography (X-CT) centre “µ-VIS”, which is part of the UK’s National facility for XCT. The centre houses some of the UK’s largest micro-focus CT scanning systems with the capability to unveil sub-surface information from an extremely wide range of materials, components and structures. With strong links between both research and industry, the centre is used for an extensive list of applications, which will offer many opportunities to apply your innovations directly to a host of relevant scientific and industrial imaging challenges.
Potential funding to support this position will be available to the strongest candidates through the Faculty of Engineering and Physical Sciences graduate school studentship programme. This funding will be awarded on a highly competitive basis.
Formal and informal training is available on a wide range of relevant scientific areas, which will be complemented with a programme in transferable skills training to prepare you for a wide range of potential careers.
Entry requirements
A UK 2:1 honours degree, or its international equivalent.
A very good undergraduate degree; at least a 1st class honours degree, or its international equivalent.
Fees and funding
Potential funding to support this position will be available to the strongest candidates through the Faculty of Engineering and Physical Sciences graduate school studentship programme. This funding will be awarded on a highly competitive basis.
We also offer a range of funding opportunities for both UK and international students. Horizon Europe fee waivers automatically cover the difference between overseas and UK fees for qualifying students.
Competition-based Presidential Bursaries from the University cover the difference between overseas and UK fees for top-ranked applicants.
Competition-based studentships offered by our schools typically cover UK-level tuition fees and a stipend for living costs, for top-ranked applicants.
For more information, please visit our postgraduate research funding pages.
How to apply
You need to:
- choose programme type (Research), 2027/28, Faculty of Engineering and Physical Sciences
- select Full time or Part time
- search for programme PhD Engineering & the Environment (7175)
- add name of the supervisor in section 2 of the application
Applications should include:
- your CV (resumé)
- 2 academic references
- degree transcripts and certificates to date
- English language qualification (if applicable)
Contact us
Faculty of Engineering and Physical Sciences
If you have a general question, doctoralcollege-admissions@soton.ac.uk.
Project leader
For an initial conversation, Thomas.Blumensath@soton.ac.uk.
The School of Engineering is committed to promoting equality, diversity inclusivity as demonstrated by our Athena SWAN award. 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. 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. The University of Southampton is committed to sustainability and has been awarded the Platinum EcoAward.