PhD Studentship: Data Fusion of X-Ray Imaging with Magnetic Induction Tomography

Application deadline: All year round

Research theme: Applied Mathematics, Imaging

How to apply: https://uom.link/pgr-apply-fap

This is a fully funded project for home students, funded by NTR-Net. The funding will cover tuition fees and provide a tax free (depending on circumstances) stipend based on the UKVI  rate (£19, 237 for 2024/25).

Identifying materials within sealed containers is of interest to the UK’s continuing stewardship of nuclear material. Radiological methods can be used to specifically identify nuclear material but are susceptible to self-shielding where the material of interest attenuates its own specific signals creating the risk of misleading results. The Alternative Signatures group in the Nuclear Threat Reduction/Nuclear Security department of the Atomic Weapons Establishment (AWE) is interested in investigating alternative methods for the specific, safe and simple identification of nuclear material even when the typical signatures of the materials may be unavailable.

X-ray imaging is commonly used to image concealed objects but x-rays are attenuated in proportion to density which reduces the available contrast when attempting to discriminate high density materials. Magnetic induction spectroscopy (MIS) explores the interaction of magnetic fields with conductive material. A magnetic induction spectrum is sensitive to the location and geometry of conductive material around a magnetic field source and receiver.

Hybrid tomography involves the fusion of two distinct imaging methods to yield a novel imaging method that combines the benefits of both. The aim of this project will be to develop a novel method for fusing the data obtained by x-ray imaging and MIS of objects relevant to AWE’s mission.

Applicants should have, or expect to achieve, at least a 2.1 honours degree or a master’s (or international equivalent) in a relevant science or engineering related discipline.

Before you apply, please email Dr Sean Holman: sean.holman@manchester.ac.uk

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