Simulating Person-specific Cognitive Processes from Expressive Behaviours for Mental Health Assessment (PhD (Funded)) Ref: 5895

Supervisors

Dr Siyang Song - Research strengths | Project ADA | University of Exeter

Prof. Andrew Howes 

The University of Exeter’s Department of Computer Science is inviting applications for a funded PhD studentship funded to commence on 21 September 2026 or as soon as possible thereafter.  For eligible students the studentship will cover Home or International tuition fees plus an annual tax-free stipend of at least £21,805 for 3.5 years full-time, or pro rata for part-time study.  The student would be based in Computer Science in the Faculty of Environment, Science and Economy at the Streatham Campus

Mental health conditions such as depression, anxiety and bipolar disorder are commonly assessed through self-report questionnaires and clinical interviews. While valuable, these methods can be subjective, time-consuming and difficult to scale. Meanwhile, mental health states may also be reflected in how people express, perceive and respond to the world through facial behaviour, speech, gaze, affective dynamics and interaction patterns.

This PhD project will develop personalised AI models for computational mental health by simulating the latent cognitive processes underlying individual expressive behaviours. The project builds on recent work showing that person-specific cognition can be computationally approximated by learning personalised neural architectures or model weights that reproduce an individual’s facial reactions to audio-visual stimuli. Rather than treating the learned model as a simple behaviour classifier, this project will use it as a representation of an individual’s internal cognitive-response mechanism for downstream mental health inference. The central hypothesis is that depression, anxiety and bipolar disorder are associated with distinctive personalised cognitive-response mechanisms that can be inferred more robustly from expressive behaviours than from surface-level behavioural features alone. Instead of directly predicting diagnostic labels from facial or speech features, the PhD will first learn an individualised cognitive simulator that models how a person generates expressive responses under emotional, social or conversational contexts. The simulated cognition will then be used to infer mental health states, symptom severity and temporal changes.

The project will pursue four objectives: developing multimodal models from facial expressions, speech, head movement, gaze and interaction dynamics; encoding simulated cognition using graph- and transformer-based methods; training and evaluating models on publicly available and ethically collected clinical or sub-clinical datasets; and investigating fairness, privacy, uncertainty estimation and explainability so that predictions are reliable and used as decision-support rather than automated diagnosis. The student will work with deep learning, affective computing, multimodal signal processing, graph neural networks, hypernetworks, temporal modelling and responsible AI. Expected outputs include personalised cognition simulation algorithms, benchmark evaluations against direct behaviour-to-label baselines, interpretable markers of cognitive-affective dysfunction, and prototypes for non-invasive mental health monitoring. Candidates should have a background in computer science, AI, machine learning, affective computing, computational psychology or related areas. Strong programming skills are essential.

International applicants will need to cover additional costs, including:

• Student visa fees
• Immigration Health Surcharge
• Relocation expenses associated with moving to the UK to undertake a PhD.

Applicants should ensure they have sufficient funds to meet these costs before applying.

The conditions for eligibility of home fees status are complex and you will need to seek advice if you have moved to or from the UK (or Republic of Ireland) within the past 3 years or have applied for settled status under the EU Settlement Scheme.  The collaboration involves a project partner who is providing funding. This means there are special terms that apply to the project. These will be discussed with Candidates at Interview and fully set out in the offer letter.  The collaboration with the named project partner is subject to contract.  Please note full details of the project partner’s contribution and involvement with the project is still to be confirmed and may change during the course of contract negotiations.  Full details will be confirmed at offer stage. 

Entry requirements

Applicants for this studentship must have obtained, or be about to obtain, a First or Upper Second Class UK Honours degree, or the equivalent qualifications gained outside the UK, in an appropriate area of Computer Science.

If English is not your first language you will need to meet the English language requirements and provide proof of proficiency. Click here for more information.

How to apply

Apply now

To apply, please click the ‘Apply Now’ button above. In the application process you will be asked to upload several documents

• CV
• Letter of application (outlining your academic interests, prior research experience and reasons for wishing to undertake the project).
• Research proposal
• Transcript(s) giving full details of subjects studied and grades/marks obtained (this should be an interim transcript if you are still studying)
• Two references from referees familiar with your academic work. If your referees prefer, they can email the reference direct to PGRApplicants@exeter.ac.uk quoting the studentship reference number.
• If you are not a national of a majority English-speaking country you will need to submit evidence of your proficiency in English.

The closing date for applications is midnight on 15/08/2026.  Interviews will be held virtually in the week commencing TBC.

All application documents must be submitted in English. Certified translated copies of academic qualifications must also be provided.

Please quote reference 5895 on your application and in any correspondence about this studentship.

Summary