DepartmentSchool of Computing
October, February and April
Applications accepted all year round
The work on this project could involve:
- Complementary forms of AI
- Transfer learning
- Practical applications in medical imaging and isotopic identification
This project will investigate the complementary use knowledge-based artificial intelligence (AI) and machine learning (ML) for image interpretation and isotopic identification. It will include the use of transfer learning to accelerate the ML training algorithms when moving from one application to another (in the case of imaging), or from one detector type to another (in the case of isotopic identification).
Our project partner, Innovative Physics Ltd (IPL), is specialised in radiation imaging systems. Application areas range from contraband detection for Customs and Excise, through to the nuclear power industry and healthcare. IPL’s technologies can detect the signature γ-ray emissions to identify chemical isotopes present within objects or in inaccessible positions. Recognition is not straightforward, as the spectra contain background noise alongside characteristics of the specific detectors deployed. Although IPL is experienced in the application of machine-learning techniques to recognise these signatures, it wishes to accelerate the learning when switching to adjunct applications and to apply knowledge-based models to apply context, understanding, and interpretation.
IPL also has broader interests in medical imaging (e.g., CT scans and MRI) and biochemical analysis (e.g., recognition of protein structures from x-ray diffraction patterns). It has the capability to generate datasets and has undertaken to make them available to this project.
Prof Hopgood has led the design and implementation of a software framework that would be suited to the complementary AI techniques for this work, subject to further development within the scope of the project. Known as DARBS (Distributed Algorithmic and Rule-based Blackboard System), it allows a variety of different software tools to work collaboratively as independent intelligent agents.
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).
Some PhD projects may include additional fees – known as bench fees – for equipment and other consumables, and these will be added to your standard tuition fee. Speak to the supervisory team during your interview about any additional fees you may have to pay. Please note, bench fees are not eligible for discounts and are non-refundable.
You'll need a good first degree from an internationally recognised university or a Master’s degree in an appropriate subject. 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.
- Background in computer science, software engineering, AI, or a related subject
- A keen interest in imaging technologies
- Excellent interpersonal and organisational skills
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.
When applying please quote project code: COMP5411021