PhD Studentship: Scatter reduction methods for high energy X-ray Computed Tomography (1148119DA)

Project Reference: ISVR-SPCG-126

Industrial Computed Tomography has seen an explosion in popularity as a non-destructive technique to assist with failure investigations, defect detection, reverse engineering, and many more. The technique is extremely powerful, but the appropriate scan configurations are always a system of compromise between, resolution, scan time, noise, contrast, artefacts etc.

The μ-VIS X-ray Imaging Centre (http://www.southampton.ac.uk/muvis/index.page), provided a dedicated centre for Industrial CT, serving a wide range of industries and applications, but with a particular focus on high energy scanning of large objects and dense materials. Operating in this regime can lead to significant artefacts due to x-ray scattering and there are various existing hardware and software methods that deal with this to a degree. This project will focus on these methods and develop novel alternatives, focusing particularly on their boundary conditions and their real world tolerances to remain effective.
You will have access to high end experimental and computing facilities, including the University’s supercomputer cluster and dedicated high specification volumetric image processing facilities. You will be hosted in the Faculty of Engineering’s µ-VIS x-ray imaging lab, one of the world’s leading x-ray facilities with its 6 complementary CT systems.

Qualifications, knowledge and experience:
The ideal candidate has a first class honours degree, an interest in experimental physics, computing and imaging.

Planning and organizing:
The candidate will be expected to have good time management, project planning and self organization.

Problem solving and initiative:
Strong analysis and problem solving skills – particular emphasis on experimental design for computational verification and simulation.

Management and teamwork:
The candidate should be comfortable working as part of an interdisciplinary team

Communicating and influencing:
Ability to communicate well in both written and oral formats.

Other skills and behaviours:
Ability to collaborate within the University and with external collaborators.

If you wish to discuss any details of the project informally, please contact Dr Thomas Blumensath Signal Processing and Control Group, Email: tb1m08@soton.ac.uk, Tel: +44 (0) 2380 59 3224. 

If you would like to apply for this project please follow the link at: https://www.southampton.ac.uk/engineering/postgraduate/research_degrees/apply.page

Closing Date: 31 Aug 2019
Post Type: PhD Studentship (Funded)