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About the Project
The Institute for the Augmented Human at the University of Bath is inviting applications for the following fully funded PhD project, which will commence 29 September 2025. An earlier start date in the 2024/25 academic year is possible, if agreed with the supervisors and Doctoral College.
This advert may close early if a suitable candidate is identified. It is therefore strongly recommended to contact the lead supervisor before applying and submit a formal application as soon as possible.
Project
The potential to control a prosthetic limb by decoding 3D limb movement intention or imagination directly from the brain using an electroencephalography (EEG) brain computer interface (BCIs) has recently been demonstrated [1],[2],[3],[4]. Decoding movement trajectories directly from EEG presents challenges, such as variable correlation (0.2<R<0.8) between movement and decoded movement trajectories, along with substantial training and calibration demands. EEG's challenges include low signal-to-noise ratio (SNR), non-stationarity, and high inter-person variability. Addressing these issues requires robust AI approaches. Roy et al. [5] systematically assessed deep learning in EEG-based BCIs highlighting its promise but also the field’s lag behind AI advancements in text, speech, and vision. The impact of large, labelled datasets and generative AI models on decoding 3D limb trajectories remains unclear however it is clear that a cooperative learning strategy within the prosthetic controller is essential to reduce reliance on the BCI user and decoder.
This project will focus on reinforcement learning (RL) [6],[7],[8],[9],[10],[11],[12] to develop a cooperative learning controller for upper-limb prostheses or exoskeletons, addressing inaccuracies in decoding neural signals. RL may be deployed in several ways to address these challenges:
The project will evaluate these possible augmentations provided by RL and deploy RL through a two-phase framework: (1) simulation-based offline training and (2) human-in-the-loop RL, incorporating user feedback via VR-based hand simulations and prosthetic exoskeletons. This hybrid system combines supervised learning for initial signal decoding with RL for fine-tuning and error correction, improving decoding accuracy and movement control for better user experience and integration into daily life.
Aligned with ongoing research, the project leverages software, AI, hardware, and experimental paradigms for trials involving augmented reality, virtual reality, and real-world prosthetic limb and/or exoskeleton manipulation. The goal is to create a robust, user-friendly system that bridges the gap between decoding accuracy and practical application, advancing BCI-driven assistive and rehabilitation technologies.
Candidate Requirements
We encourage applicants from any science or engineering background.
Applicants should hold, or expect to receive, a First Class or good Upper Second Class Honours degree (or the equivalent). A master’s level qualification would also be advantageous.
Non-UK applicants must meet our English language entry requirement.
Enquiries and Applications
Informal enquiries are encouraged! Direct these to Prof Damien Coyle - dhc30@bath.ac.uk
Please make a formal application should via the University of Bath’s online application form for a PhD in Computer Science.
When completing the form, in the 'Funding your studies' section, please select 'Donor or Alumni Funded Studentship' from the first drop-down list and specify Nick Hynes PhD Scholarship in the text box. In the 'Your PhD project' section, please state the project title and supervisor's name in the appropriate boxes.
Failure to complete these steps will delay the processing of your application and may cause you to miss the deadline.
More information about applying for a PhD at Bath may be found on our website.
Equality, Diversity and Inclusion
We value a diverse research environment and strive to be an inclusive university, where difference is celebrated and respected. We encourage applications from under-represented groups.
If you have circumstances that you feel we should be aware of that have affected your educational attainment, then please feel free to tell us about it in your application form. The best way to do this is a short paragraph at the end of your personal statement.
The Disability Service ensures that individuals with disabilities are provided the support that they need. If you state if your application that you have a disability, the Disability Service will contact you as part of this process to discuss your needs.
Keywords: AI, Reinforcement learning, EEG, EMG, neurotechnology, brain-computer interface, exoskeleton, prosthetic limb, motion trajectory prediction/decoding estimation
Applicants will be considered for a fully funded 4 year Nick Hynes PhD studentship for the augmented human. This will cover the tuition fee at the Home or Overseas rate, a maintenance stipend at the UKRI rate (£19,237 in 2024/25) and a training and research budget of £5000 per annum. More info here: View Website
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Research output data provided by the Research Excellence Framework (REF)
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