Machine learning for hyperspectral computed tomography

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

Start date: September 2019.

Further Information: 

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

Closing Date: 

Thursday, February 28, 2019
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Principal Supervisor: 


Minimum entry qualification - a 1st class Honours degree (or International equivalent) in a relevant science or engineering discipline, supported by an MSc Degree. Further information on English language requirements for EU/Overseas applicants.


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

Further information and other funding options.

Informal Enquiries: