Elsewhere on Ulster
This project is funded by:
Many modern technologies — from medical brain-computer interfaces (BCIs) to automated factories — use artificial intelligence to learn from data and make decisions.
However, the real world is always changing. Brain signals vary over time, and machines wear out or behave differently as they age.
As a result, today’s AI systems often struggle to remain accurate once deployed, and require frequent manual retraining by experts. This is inefficient, costly, and limits their use in everyday settings.
This project aims to develop self-maintaining AI — systems that can independently recognise when they need to update and adapt only when it is genuinely useful.
This approach will avoid wasting energy on unnecessary retraining and help AI stay reliable over long periods without expert supervision.
A key research element is the use of digital twins — virtual “replicas” of real systems that will update in real-time as the system evolves and help predict problems early and guide safe, efficient adaptation.
The first area of application is BCIs for people living with neurological conditions that affect movement e.g. stroke and motor neuron disease.
Self-maintaining AI could help such BCIs work safely and smoothly at home, supporting rehabilitation and independence.
The second area is smart manufacturing, where self-maintaining AI can improve safety, reduce downtime, and lower energy usage by detecting faults early and adapting equipment control intelligently.
Overall, this research will help create trustworthy and sustainable AI systems that continue to perform well in the real world — improving quality of life for vulnerable people and supporting responsible industrial innovation.
The successful candidate will benefit from Ulster’s wide-ranging expertise in Computational Neuroscience, Neurotechnology, and Machine Learning and access to cutting-edge neuroimaging facilities such as NIRS/EEG/MEG and will interact with leading international collaborators.
The student will gain valuable knowledge and training in mathematics, machine learning, high-performance computing, and brain sciences.
Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.
We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.
In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.
If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.
The University is an equal opportunities employer and welcomes applicants from all sections of the community, particularly from those with disabilities.
Appointment will be made on merit.
This project is funded by:
This scholarship will cover tuition fees and provide a maintenance allowance of £21,000* (tbc) per annum for three years (subject to satisfactory academic performance). A Research Training Support Grant (RTSG) of approximately £900 per annum is also available.
To be eligible for these scholarships, applicants must meet the following criteria:
Applicants should also meet the residency criteria which requires that they have lived in the EEA, Switzerland, the UK or Gibraltar for at least the three years preceding the start date of the research degree programme.
Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.
Due consideration should be given to financing your studies.
*Part time PhD scholarships may be available, based on 0.5 of the full time rate, and will require a six year registration period
* Raza, Prasad, & Li, (2015). EWMA Model based Shift-Detection Methods for Detecting Covariate Shifts in Non-Stationary Environments. Pattern Recognition, 48 (3). pp. 659-669. https://doi.org/10.1016/j.patcog.2014.07.028.
* Youssofzadeh, Zanotto, Wong-Lin, Agrawal, Prasad (2016). Directed Functional Connectivity in Fronto-Centroparietal Circuit Correlates with Motor Adaptation in Gait Training. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 24(11), https://doi.org/10.1109/TNSRE.2016.2551642
* Friston, FitzGerald, Rigoli, Schwartenbeck & Pezzulo (2017). Active inference: A process theory. Neural Computation, 29(1):1–49, 2017. doi:10.1162/NECO_a_00912
* Chowdhary, Raza, Meena, Dutta & Prasad (2017). Online Covariate Shift Detection based Adaptive Brain-Computer Interface to Trigger Hand Exoskeleton Feedback for Neuro-Rehabilitation. IEEE Transactions on Cognitive and Developmental Systems, 10(4), DOI: 10.1109/TCDS.2017.2787040.
* Alippi, C., Boracchi, G., & Roveri, M. (2017). Hierarchical Change-Detection Tests. IEEE Transactions on Neural Networks and Learning Systems, 28(2), 246–258.
* Gaur, McCreadie, Pachori, Wang, & Prasad (2019). Tangent space features-based transfer learning classification model for two-class motor imagery brain-computer interface. International Journal of Neural Systems (IJNS). https://doi.org/10.1142/S0129065719500254
* Kudithipudi, D., Aguilar-Simon, M., Babb, J. et al. (2022). Biological underpinnings for lifelong learning machines. Nat Mach Intell 4, 196–210 https://doi.org/10.1038/s42256-022-00452-0.
* Hurtado, Salvati, Semola et al. (2023), Continual learning for predictive maintenance: Overview and challenges. Intelligent Systems with Applications 19 (2023) 200251. https://doi.org/10.1016/j.iswa.2023.200251
Submission deadline
Friday 27 February 2026
04:00PM
Interview Date
tbc
Preferred student start date
14th September 2026
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