Project code



School of Computing

Start dates

October, February and April

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 or February.

The PhD will be based in the School of Computing and will be supervised by Dr Honghai Liu and Dr Zhaojie Ju.

Machine enhanced therapy/intervention for children with ASD has been propelled by the innovations in human-machine interfaces and computer vision. Impressive results in the sensing and analytics of ASD children’s behaviour have enabled the avenue for more dexterous interaction taking into account their less preference for interacting with non-human agents. Despite the significant attention in machine assisted healthcare for children with ASD, an educational purpose targeted machine assisted system is still missing. The knowledge and valuable data from machine enhanced therapy/intervention has not been converted into actionable application in special education yet.

The goal of this PhD project is to develop a better understanding of how machine assisted education systems are more effective at a reduced burden of human intervention and build a machine assisted education system for children with ASD. To achieve this aim, the state-of-the-art human behaviour sensing and analytics techniques will be transformed into a real application with an emphasis on the curriculum design, affective computing and system integration. The outcome of this PhD project will enable the special education school users to reduce their repeated workload in daily teaching while observing the progress of children behaviour/knowledge with quantitative measurements.

The project will involve building a virtual environment based curriculum and knowledge visualisation in the special education domain of ASD, developing an affective computing framework for children behaviour analysis comprising gaze estimation, expression recognition and motion recognition, and the contactless sensory system integration for an education targeted platform. Based on the tangible system, a long-term evaluation of the machine assisted education for children with ASD will be conducted in special education schools. Experiments will be run to assess how effectively the burden of teachers is reduced and how ASD children benefit from the machine assisted teaching of knowledge and skills.

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).

Bench fees

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.

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 an Civil Engineering or 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.

The candidate should have a UK Honours Degree at 2.1 (or equivalent) in Computing Science or related area. A good understanding of OpenCV and related programming skills are ideally preferred for shortlisting the candidates.

How to apply

We’d encourage you to contact Dr Honghai Liu ( to discuss your interest before you apply, quoting the project code CCTS4540219.

When you are ready to apply, please follow the 'Apply now' link on the Health Informatics 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.