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Machine learning for hyperspectral computed tomography

  • Full or part time
  • Application Deadline
    Thursday, February 28, 2019
  • Funded PhD Project (European/UK Students Only)
    Funded PhD Project (European/UK Students Only)

Project Description

Spectral/hyperspectral tomography aims to improve standard Computed Tomography by taking advantage of additional information in the form of energy-resolved measurements. The objective is to use spectral information to reduce artifacts and improve the image reconstruction in cases of limited-angle projections. To this end, this project will explore the application of machine/deep learning methods in combination with classical and Bayesian inversion methods. Applications will include non-destructive testing of materials and security screening. This project will run in collaboration with Harris Corporation and colleagues at the Alan Turing Institute.

Your responsibilities:
• explore ideas, methods and algorithms in the use of machine learning techniques for hyperspectral CT
• implement algorithms in programming languages (Python, MATLAB)
• present results at international conferences and in scientific publications
• work in a team with other PhD students, postdocs, and staff.

Your qualifications:
• Master’s degree (or equivalent) in computational science or scientific computing or similar degree with a focus on applied mathematics and computing
• knowledge/experience in inverse problems is an advantage
• interest/experience in machine/deep learning
• programming skills in Python or MATLAB
• fluent in spoken and written English

Our group’s research interests are in computational modelling of physical and engineering systems and inverse problems for imaging, process monitoring, and non-destructive testing. This area entails mathematical modelling, statistical inference and optimisation algorithms. For more info see

Start date: September 2019

Funding Notes

Minimum entry qualification - a 1st class Honours degree (or International equivalent) in a relevant science or engineering discipline, supported by an MSc Degree.

Tuition fees and stipend are available for Home/EU students (International students can apply, but the funding only covers the Home/EU fee rate).

How good is research at University of Edinburgh in General Engineering?
(joint submission with Heriot-Watt University)

FTE Category A staff submitted: 91.80

Research output data provided by the Research Excellence Framework (REF)

Click here to see the results for all UK universities

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