About this position
Micro-expressions are tiny, involuntary facial movements that can occur when a person tries to hide an emotion, but are difficult to analyse because they are subtle, short-lived, and easily confused with ordinary facial movement. While many methods have been developed to recognise and localise micro-expressions, these results remain difficult to understand.
The proposed models will be designed to identify where the relevant facial evidence occurs, describe what subtle change has been observed, and explain why a prediction has been made.
The project will deliver new methods, benchmark protocols, and evaluation frameworks for explainable and trustworthy micro-expression analysis, with relevance to human-centred AI, and healthcare-related affect analysis. The core novelty of the project is to shift micro-expression research from predominantly vision-only pipelines towards multimodal foundation-model approaches that combine subtle visual motion with structured language-level representations such as action units, temporal descriptions, prompt templates, and evidence-based explanations.
Objectives
We will investigate whether vision-language models (VLMs) can improve micro-expression analysis by combining video understanding with language-based reasoning.
- Process micro-facial expression data more efficiently in computer vision and vision language models.
- Create a language-guided representation for subtle facial motion, exploring multimodal data
- Create a spot-then-recognise approach to detect micro-expression intervals and interpret language-aware recognition.
- Evaluate novel methods and validate against the state-of-the-art in a real-world setting.
Funding
These are doctoral teaching assistant positions that combine a PhD programme with a university teaching contract. Your time will be split approximately 60% on research and 40% on teaching. This provides excellent preparation for candidates considering an academic career at a university.
The teaching component will typically run over the 22 teaching weeks per year and the 4 assessment weeks. You will help deliver an outstanding student experience by supporting lead academics with classroom and lab teaching and assessment, further building the skills developed within your PhD research programme.
The position is grade 6 with a current salary of £31,236 and includes payment of home PhD tuition fees for the duration of the 6-year award. Home students can apply. Applicants must have the right to work in the UK. We are unable to offer visa sponsorship for this role.
Candidate requirements
Candidates must have expertise in developing computer vision and machine learning algorithms. Vision Language Model (VLM) experience is desirable.
Qualifications
- A high-grade undergraduate degree (first class or upper second) in computer science or MSc in related field.
Skills
- Knowledge of programming, computer vision, machine learning, or related discipline.
- Experience with Python and relevant AI/ML libraries (such as PyTorch, TensorFlow).
- Demonstrated knowledge of multimodal data processing (such as vision, language, 3D).
How to apply
If you have any questions, contact the principal supervisor, Dr Adrian Davison .
To apply you will need to complete the online application form for a part time PhD in Computing & Digital Technology.
Please complete the Doctoral Project Applicant Form , and include your CV and a covering letter to demonstrate how your skills and experience map to the aims and objectives of the project, the area of research and why you see this area as being of importance and interest.
Please upload these documents in the supporting documents section of the University’s Admissions Portal or send them to the PGR Admissions team at pgradmissions@mmu.ac.uk .
Please quote the reference: SciEng-DTA Jan 2027-AD-Micro Expression Vision
Application link: PhD Computing and Digital Technology — six years part-time
£31,236 - please see advert
Funding
GBP 31,236 per year
How to apply
- Read the full advert on the source site — it carries the authoritative terms.
- Email the contact below with your CV and a short, specific message. See Emailing Professors.
- Prepare your SOP, CV, transcripts and referees before the deadline.
- Apply through the university's own portal. Never pay a fee to a third party.