PhD project description: Inner speech is functionally involved in memory, planning, and self-regulation. It also manifests as verbal rumination, a feature of depression and anxiety, and is implicated in voice-hearing experiences in psychosis. Existing approaches mostly use silent-speech paradigms in which inner speech is produced on cue, but ruminative and intrusive thoughts arise spontaneously, so capturing them requires methods that work in naturalistic conditions.
This PhD will develop such methods, combining wearable EEG with portable ultrasound imaging of the tongue. The student will work at the intersection of cognitive/computational neuroscience, machine learning, and articulatory phonetics. They will translate an existing inner-speech classifier from research-grade laboratory EEG to a wearable form factor, and develop deep-learning models that identify spontaneous inner-speech episodes and characterise their emotional valence. The project will also establish what is achievable with wearable EEG alone, a critical step towards future clinical applications.
The student will be based in the Language, Inner Speech, and Neuroscience (LISN) Lab within the Department of Psychology, and embedded in the Imaging and Computer Vision Lancaster (iCVL) group (Computing) and the Phonetics Lab (Linguistics). They will be part of the DSAIL doctoral cohort, which brings together AI and data-science PhD students from across the university, with its own training programme, annual research events, and computational resources including Lancaster’s High-End Computing (HEC) GPU cluster. The Department of Psychology holds an Athena Swan Silver Award and supports an active research culture of seminars, writing workshops, and PhD-led activities.
Training and skills: Supervision is interdisciplinary, spanning cognitive neuroscience, machine learning, and articulatory phonetics. The student will develop hands-on skills in experimental design for EEG, ultrasound, and behavioural data collection; deep-learning model development; multimodal signal processing and fusion; articulatory speech analysis; and reproducible research practice with open-source software release. Outputs from the project will span cognitive neuroscience, machine learning, and phonetics venues.
Application details: The successful candidate will hold, or expect to obtain, at least an upper-second-class honours degree, and ideally a Masters, in computational neuroscience, computer science, or a closely related quantitative discipline. Strong Python programming skills and demonstrable machine-learning experience are essential. Applicants should complement this technical expertise with familiarity with neuroscientific or psychological experimental design - for example, in language, cognition, or inner speech research. Prior experience in signal processing, deep learning, and/or EEG and neuroimaging analysis is highly desirable.
Funding details: This 3.5-year full-time studentship is open to UK home students. It covers UK home fees, a tax-free stipend at the UKRI minimum rate for 2026/27 (£21,805 p.a.), paid subject to satisfactory progress, and a Research Training Support Grant (RTSG) of £1,000 per year for research-related expenses, conferences, and training. Additional cohort training and computational resources are provided through DSAIL.
How to apply (note there are 2 elements to the process): Please apply though the official University process. Instructions for applying through the University can be found at the Ãå±±ÂÖ¼é Admission Portal. Please also submit your application materials via this form, which is specific to this PhD opportunity.
The materials required are:
- Academic CV along with the names and contact details of two referees who will be contacted directly (at least one must be academic).
- A sample of your work, either a code portfolio (e.g., a GitHub repository, a machine-learning project write-up, or other evidence of independent technical work) or an academic writing sample (e.g., an undergraduate or Masters dissertation, a coursework essay, or a peer-reviewed journal article). Both are welcome if you have them.
- A two-page personal statement / cover letter explaining your motivation and readiness for undertaking this PhD project.
- For the LU application, in place of a Research Proposal, you may upload a copy of this advertisement.
Dates
- Application deadline: 30 June 2026
- Provisional interview date: week beginning 20 July 2026
- Studentship start date: 1 October 2026
Informal enquiries: Prospective applicants are warmly encouraged to get in touch before applying. Please contact Dr Bo Yao (b.yao1@lancaster.ac.uk) for informal enquiries about the project, the supervisory team, or the application process.
