UOS
Intelligent X-ray computed tomography scanning for advanced manufacturing
✓ Funded (Competition)
⏰ Closing Soon
🎓 Aerospace Engineering
🎓 Automotive Engineering
🎓 Computer Vision
🎓 Electrical Engineering
🎓 Machine Learning
🎓 Manufacturing Engineering
🎓 Mechanical Engineering
robotics
x-ray tomography
AI optimisation
XCT
advanced manufacturing
computer vision
digital twins
image processing
non-destructive testing
simulation
Funded PhD at the University of Southampton developing AI-driven X-ray CT scanning strategies for complex engineering components. Combines robotics, simulation, and image processing to improve non-destructive inspection for aerospace and advanced manufacturing applications.
Project Description
This PhD project focuses on advancing X-ray computed tomography (XCT) to address the growing challenges of inspecting complex engineering components. XCT is widely used for non-destructive 3D imaging of internal structures, particularly in safety-critical industries such as aerospace, automotive, and energy.
Conventional XCT systems rely on fixed scanning configurations and circular trajectories, which become inefficient for large or geometrically complex objects such as aircraft structures or additively manufactured components. This project aims to overcome these limitations by developing intelligent and adaptive scanning strategies.
Key research directions include:
Developing AI-driven scan optimisation methods
Exploring robotic manipulators for flexible scanning geometries
Creating digital twins for simulation and optimisation of scanning processes
The project will involve a combination of experimental work and computational modelling, with a strong emphasis on image processing and data analysis. The research will contribute to improving inspection accuracy, efficiency, and reliability in advanced manufacturing systems.
Entry Requirements
A UK 2:1 honours degree (or international equivalent) in engineering, physics, or related field
Strong programming skills
Experience with image processing (Python or MATLAB preferred)
Interest in AI, simulation, or computational modelling
Strong programming skills
Experience with image processing (Python or MATLAB preferred)
Interest in AI, simulation, or computational modelling
How to Apply
Apply via the University of Southampton postgraduate research portal:
Steps:
Select programme: PhD Engineering & the Environment (7175)
Choose Research (2026/27 entry)
Add supervisor name in application
Required documents:
CV
Two academic references
Degree transcripts and certificates
English language qualification (if applicable)
For enquiries:
General: feps-pgr-apply@soton.ac.uk
Supervisor: J.J.Lifton@soton.ac.uk
Steps:
Select programme: PhD Engineering & the Environment (7175)
Choose Research (2026/27 entry)
Add supervisor name in application
Required documents:
CV
Two academic references
Degree transcripts and certificates
English language qualification (if applicable)
For enquiries:
General: feps-pgr-apply@soton.ac.uk
Supervisor: J.J.Lifton@soton.ac.uk
Eligibility
UK/Home
EU
International
Supervisor Profile
JL
Joseph Lifton
University of Southampton, Faculty of Engineering and Physical Sciences
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