Supervisory Team: Professor Thomas Blumensath, Dr Joe Lifton, Dr Richard Boardman and Dr Mark Mavrogordato
In this industry sponsored project, you will develop advanced machine learning tools to detect and remove scatter artefacts from X-ray images acquired with state of the art, high energy X-ray imaging systems, where scatter becomes the dominant source of image artefact.
X-rays are used extensively to visualise the inside of objects. Not only are they one of the key modalities used in medical imaging, they are also becoming increasingly important in industrial applications where they are a key technology for the non-destructive inspection of complex components and assemblies. X-ray based computed tomography (XCT) is a particularly valuable engineering tool that can provide high resolution and magnified 3D representations of internal object geometries, quantitative information and defect mapping. XCT enables the inspection of complex components and assemblies and thus guarantees safety, accuracy and/or efficiency in virtually every major industry. Unfortunately, using commonly available X-ray tomography systems, object size currently remains a major limiting factor, particularly for objects made of materials such as metals, which are relatively opaque to X-rays. Standard commercial X-ray tomography systems generate X-rays with energies in the kilo electron volt (keV) range, which significantly limits the amount of material they can penetrate. An alternative is to use more powerful X-ray sources that generate X-rays with energies in the MeV region. Whilst MeV linear accelerator sources are now becoming more generally available for industrial inspection and whilst experimental laser based X-ray sources are being build in dedicated scientific research labs, a range of challenges remain preventing their routine use. This is partly due to a change in the relevant physical principles that govern the interactions between high energy (MeV) X-rays and matter. X-ray images acquired at these energies become increasingly contaminated by scattered X-ray radiation, which obscures and distorts relevant information. This project is one of two related, industry sponsored PhDs that will look at the reduction and removal of the scatter signal from X-ray tomographic data acquired with MeV sources. The novel aspect of this project will be the investigation of advanced machine learning methods to reduce and remove the effect of X-ray scattering from high energy X-ray tomographic data. These computational methods predict, and thus remove, the contribution of the scattered X-ray radiation. Two approaches are feasible a) predicting the scatter signal directly from the observed data or, b) using the knowledge of the forward propagation of X-rays through materials, coupled with estimated or know object geometries, to simulate (and then remove) the X-ray scatter signal. Using simulated and real X-ray images, this project will study and modify these approaches and study how they perform when applied to high energy (>MeV) X-ray tomography data. The successful candidate will be working alongside the team at the University of Southampton’s µ-VIS facility, a dedicated X-ray Computed Tomography (XCT) centre. The centre is part of the UK’s National Facility for lab-based XCT and houses some of the UK’s largest micro-focus CT scanning systems, capable of unveiling sub-surface information from materials, components and structures. With strong links between both research and industry, the centre itself is used for aircraft crash investigations, Formula 1, space technology and much more – please see our website for further info: www.muvis.org.
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.
This is sponsored by AWE Nuclear science and technology. Candidates need to be UK citizens and should be eligible to achieve the highest UK security clearance.
Entry Requirements
A very good undergraduate degree (at least a UK 2:1 honours degree, or its international equivalent, with internal studentship funding likely to require a first class or strong 2:1 honours degree).
Closing date: 31 March 2026. Applications will be considered in the order that they are received, the position will be considered filled when a suitable candidate has been identified.
Funding: Due to requirements by the industrial sponsor, funding for this project is only available for UK nationals. This project is co-sponsored through University funds available on a competitive basis. An enhanced stipend will be available for an eligible candidate.
How To Apply
Apply online: Search for a Postgraduate Programme of Study (soton.ac.uk)
Programme type: Research
• Academic year: 2026/27
• If you will be full time or part time
• Faculty: Engineering and Physical Sciences
Search for programme PhD Engineering & the Environment (7175)
Please add the name of the supervisor in section 2 of the application.
Applications should include:
• your CV (resumé)
• 2 academic references
• degree transcripts/ certificates to date
• English language qualification (if applicable)
For further information please contact: feps-pgr-apply@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.