Project code



School of Computing

Start dates

February and October

Application deadline

Applications accepted all year round

Applications are invited for a self-funded, 3 year full-time or 6 year part-time PhD project, to commence in October 2020 or February 2021.

This PhD position is being offered as part of a collaboration between the University of Portsmouth (contact Professor Adrian Hopgood), Loughborough University (contact Dr Sarah Bubgy), and the Open University (contact Dr Jon Hall).

Applicants may choose to base this PhD at any of these sites and should apply to the contact listed for their chosen institution. For relevant projects available at Loughborough or the OU please contact the appropriate person above. If the appropriate funding is available, a successful applicant would be able to undertake placements at project partners during their PhD. Additional bench fees may be required, please contact your proposed supervisor to discuss.

This project will include experimental work in a laboratory and there is scope to collaborate with partners across institutions and sectors (academic, industrial, clinical).


The work on this project will:

  • Aim to improve medical diagnosis and treatment.
  • Combine AI and physics
  • Involve a range of range of AI including machine learning and knowledge-based techniques.
  • Involve analysis of image formation in compound semiconductor detectors.

Nuclear medicine is an important branch of medical physics and is used to diagnose and treat cancers, among other conditions. There is a constant drive to improve the quality of imaging technology, as better images have a direct benefit to patient outcome and may even enable new diagnostic tests to be developed.

Artificial intelligence (AI) and machine learning are of great interest to the medical community and research is ongoing in a wide range of areas, including distinguishing between benign and cancerous tumours and improving detector performance. However, the nature of AI techniques often leads to ‘black box’ analysis software, which is a particular danger in medical imaging where analysis errors could be catastrophic. During this project you will develop and analyse machine-learning derived algorithms and compare these to known physical processes with the goal of opening these black boxes – deriving methods that may have applications in a wide range of areas.

When designing detectors for nuclear medicine there is often a trade-off to be made between energy resolution and sensitivity – both important parameters for medical imaging. This project will involve the application of AI and machine-learning techniques to data from detectors for nuclear medicine, to improve energy resolution while maintaining sensitivity.

Recent developments in compound semiconductor detectors are of great interest for nuclear medicine, where their improved energy resolution over traditional detectors could allow the use of novel dual-isotope imaging techniques. 

When detecting high energy photons (>100keV), as is the case for nuclear medicine applications, depth of interaction effects and charge sharing can lead to a degradation in energy resolution. For detectors with small pixel sizes, a single detected photon may produce a signal across multiple pixels. The pattern of this signal is correlated to the amount of energy lost when this event is reconstructed. However, the large range of possible patterns means this reconstruction is difficult to solve using traditional techniques.

Artificial intelligence can provide a solution to this problem. The tools of AI can be roughly divided into knowledge-based and data-based techniques. The latter include machine learning algorithms based on large neural networks, which can recognise signature patterns after training on historical data. The knowledge-based techniques provide an explicit representation of specialist knowledge, in this case the physics of photon interactions within the detector. The knowledge-based techniques add context and sense-checking to the classification from the machine-learning algorithms. In this way, the project will link the analysis of machine-learning derived algorithms to the fundamental physical processes in the detector.

Fees and funding

Visit the research subject area page for fees and funding information for this project.

Funding availability: Self-funded PhD students only. 

PhD full-time and part-time courses are eligible for the UK Government Doctoral Loan (UK and EU students only).

Entry requirements

You'll need a good first degree from an internationally recognised university (minimum upper second class or equivalent, depending on your chosen course) or a Master’s degree in Physics or Computer Science or a related area. In exceptional cases, we may consider equivalent professional experience and/or qualifications. English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.


How to apply

We’d encourage you to contact Prof Adrian Hopgood ( to discuss your interest before you apply, quoting the project code.

How to apply

When you are ready to apply, please follow the 'Apply now' link on the Computing PhD subject area page and select the link for the relevant intake. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV. Our ‘How to Apply’ page offers further guidance on the PhD application process. 

October start

Apply now

February start

Apply now

When applying please quote project code: COMP5450620