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The University of Manchester

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PhD in Embodied Brain–Computer Interfaces for Adaptive Human–AI Interaction The University of Manchester in United Kingdom

Degree Level

PhD

Field of study

Computer Science

Funding

This is a 3.5-year PhD. It is primarily for self-funded students, but exceptional candidates may be considered for School funding, which includes an annual tax-free stipend of £21,805 for 2026/27 and tuition fees paid. Stipend is expected to increase each year.

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Country

United Kingdom

University

The University of Manchester

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Keywords

Computer Science
Cognitive Science
Biomedical Engineering
Electrical Engineering
Information Technology
Human-computer Interaction
Eeg
Neuropsychology
Reinforcement Learning
Robotics
Brain-computer Interface
ML

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About this position

PhD opportunity at The University of Manchester, Department of Computer Science in Embodied Brain–Computer Interfaces for Adaptive Human–AI Interaction.

This project sits at the intersection of computer science, artificial intelligence, human-computer interaction, brain-computer interfaces, robotics, and neural signal processing. The research aims to move beyond traditional one-way BCI control and develop closed-loop, adaptive human–AI interaction where both the user and the embodied agent adapt over time.

Possible methods include EEG, eye movements, muscle activity, motion signals, multimodal representation learning, shared-control policies, uncertainty-aware neural decoding, online adaptation, and reinforcement learning. The embodied platform may be a virtual environment, assistive device, robot, or interactive avatar depending on the candidate’s interests and the research questions.

The project emphasizes evaluation of interactive systems, including decoding reliability, response time, task performance, cognitive demand, adaptability, accessibility, privacy, and user experience. The expected outcome is a new computational and experimental framework for embodied BCIs, along with prototype systems and evidence-based design principles for safe, adaptive, human-centred neural interfaces.

Eligibility: applicants with backgrounds in computer science, AI, electrical/electronic engineering, biomedical engineering, robotics, neuroscience, HCI, or related disciplines are encouraged to apply. Strong quantitative and programming skills are desirable, and experience with machine learning, neural/physiological signal processing, BCIs, robotics, VR/AR, human-robot interaction, or human-participant studies is preferred.

Funding: the PhD is advertised as self-funded, but exceptional candidates may be considered for School funding that includes a tax-free stipend of £21,805 for 2026/27 plus tuition fees.

Application: applications are accepted all year round, but the advert may close once filled. Applicants are strongly advised to contact the supervisor before applying and to submit the required documents through the university portal.

Funding details

This is a 3.5-year PhD. It is primarily for self-funded students, but exceptional candidates may be considered for School funding, which includes an annual tax-free stipend of £21,805 for 2026/27 and tuition fees paid. Stipend is expected to increase each year.

What's required

Applicants should have a background in computer science, artificial intelligence, electrical or electronic engineering, biomedical engineering, robotics, neuroscience, human-computer interaction, or a closely related discipline. A strong quantitative foundation and programming experience are desirable. Relevant experience in machine learning, neural or physiological signal processing, brain-computer interfaces, robotics, virtual or augmented reality, human-robot interaction, or experimental studies with human participants is preferred. Python and machine-learning frameworks would be advantageous. Experience with EEG or other neural data, embodied agents, assistive technologies, or interactive robotic systems is especially suitable, though expertise across all areas is not required.

How to apply

Contact supervisor Jingyuan Sun before applying and include your current study level, academic background, relevant experience, and a motivation paragraph. Then apply online via the university portal and provide the project title, supervisor name, funding status, previous study details, and two referees. Upload all required supporting documents, including transcripts, CV, supporting statement, referees, and English language certificate if applicable.

More information can be found here

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