About the Project
Brain–computer interfaces (BCIs) are commonly developed as systems that translate neural signals into isolated commands, often in highly controlled laboratory settings. However, intelligent systems increasingly operate through virtual avatars, wearable devices and physical robots that continuously perceive and act in the world. This project will investigate how BCIs can be integrated with embodied artificial intelligence to create adaptive, closed-loop forms of human–AI interaction.
The central aim is to develop computational methods that jointly model neural activity, human behaviour, the state of an embodied agent and the surrounding environment. Rather than treating a BCI as a one-way control channel, the project will explore interaction in which both the person and the artificial agent adapt over time. For example, an embodied agent might use neural signals to estimate intended actions, cognitive workload or uncertainty, while combining this information with visual, behavioural and environmental context before selecting an appropriate response.
The research may involve non-invasive neural recordings such as EEG, together with complementary signals including eye movements, muscle activity, motion or task context. Possible methodological directions include multimodal representation learning, shared-control policies, uncertainty-aware neural decoding, online adaptation and reinforcement learning. The embodied platform could involve a virtual environment, assistive device, robot or interactive avatar, depending on the research questions and the candidate’s interests.
A key part of the project will be evaluating the resulting systems as interactive technologies rather than only as signal-classification algorithms. Relevant outcomes may include decoding reliability, response time, task performance, cognitive demand, adaptability and user experience. The project will also consider accessibility, privacy and the appropriate distribution of control between the person and the artificial agent.
The expected outcome is a new computational and experimental framework for embodied BCIs, supported by prototype systems and evidence-based principles for designing safe, adaptive and human-centred neural interfaces.
Eligibility
Applicants with a background in computer science, artificial intelligence, electrical or electronic engineering, biomedical engineering, robotics, neuroscience, human–computer interaction or a closely related discipline are particularly encouraged to apply.
A strong quantitative foundation and experience in programming are desirable. Preference will be given to applicants with relevant experience in at least one of the following areas: machine learning, neural or physiological signal processing, brain–computer interfaces, robotics, virtual or augmented reality, human–robot interaction, or experimental studies involving human participants. Experience with Python and machine-learning frameworks would be advantageous.
Applicants who have previously worked with EEG or other neural data, embodied agents, assistive technologies or interactive robotic systems will be especially well suited to the project. However, applicants are not expected to have expertise across all of these areas. Training can be provided in the neural-interface, embodied-AI or experimental components according to the successful applicant’s existing background.
Funding
This 3.5-year PhD is for self-funded students. Exceptional candidates will be considered for School funding (this will include an annual tax-free stipend of £21,805 for 2026/27 and tuition fees will be paid. We expect the stipend to increase each year). The proposed start date is July 2027.
We recommend that you apply early as the advert will be removed once the position has been filled.
Before you apply
We strongly recommend that you contact the supervisor jingyuan.sun@manchester.ac.uk for this project before you apply. Please include details of your current level of study, academic background and any relevant experience and include a paragraph about your motivation to study this PhD project.
How to apply
Apply online through our website: https://uom.link/pgr-apply-2425
When applying, you’ll need to specify the full name of this project, the name of your supervisor, if you already having funding or if you wish to be considered for available funding through the university, details of your previous study, and names and contact details of two referees.
Your application will not be processed without all of the required documents submitted at the time of application, and we cannot accept responsibility for late or missed deadlines. Incomplete applications will not be considered.
After you have applied you will be asked to upload the following supporting documents:
- Final Transcript and certificates of all awarded university level qualifications
- Interim Transcript of any university level qualifications in progress
- CV
- Supporting statement: A one or two page statement outlining your motivation to pursue postgraduate research and why you want to undertake postgraduate research at Manchester, any relevant research or work experience, the key findings of your previous research experience, and techniques and skills you’ve developed. (This is mandatory for all applicants and the application will be put on hold without it).
- Contact details for two referees (please make sure that the contact email you provide is an official university/work email address as we may need to verify the reference)
- English Language certificate (if applicable)
If you have any questions about making an application, please contact our admissions team by emailing FSE.doctoralacademy.admissions@manchester.ac.uk.
Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. We know that diversity strengthens our research community, leading to enhanced research creativity, productivity and quality, and societal and economic impact.
We actively encourage applicants from diverse career paths and backgrounds and from all sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation and transgender status.
We also support applications from those returning from a career break or other roles. We consider offering flexible study arrangements (including part-time: 50%, 60% or 80%, depending on the project/funder).
Funding Notes
This 3.5-year PhD is for self-funded students. Exceptional candidates will be considered for School funding (this will include an annual tax-free stipend of £21,805 for 2026/27 and tuition fees will be paid. We expect the stipend to increase each year). The proposed start date is July 2027.
We recommend that you apply early as the advert will be removed once the position has been filled.