Real-world environments are often non-stationary. Learning systems such as classifiers used for pattern recognition make the stationarity assumption in input data distribution density during training and operation phases. As a result, their field performance may become unsatisfactory and therefore such learning systems may have only limited practical use.
This project aims to investigate how non-stationarities can be effectively accounted for in an automated way, in a wide-range of evolving systems. It will involve developing techniques for learning from the changes in inputs alone, possibly based on transfer learning using archived data; devising adaptation strategies that can make effective use of the available information to extract knowledge about the system variability and also making continuous update to account for the new information. A major focus will be on thorough evaluation of the method in multiple application areas including brain-computer interfaces.
The successful PhD candidate will benefit from Ulster’s wide-ranging expertise in Computational Neuroscience, Cognitive Neuroscience, Machine Learning and Computational Biology, and will interact closely with various leading international collaborators. The student will gain valuable knowledge in data mining and machine learning techniques, high-performance computing, statistics, and brain sciences. These are essential in many areas of science, engineering, mathematics, and health and biomedical sciences. This training will provide wide opportunities for finding skilled work, especially in the burgeoning field of AI and data science.
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 offers the following levels of support:
Full award (full-time PhD fees + DfE level of maintenance grant + RTSG for 3 years).
This scholarship will cover full-time PhD tuition fees and provide the recipient with £18,000 (tbc) maintenance grant per annum for three years (subject to satisfactory academic performance).
This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.
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.
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.
Part award (full-time PhD fees + 50% DfE level of maintenance grant + RTSG for 3 years).
This scholarship will cover full-time PhD tuition fees and provide the recipient with £8,000 maintenance grant per annum for three years (subject to satisfactory academic performance). This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.
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.
Fees only award (PhD fees + RTSG for 3 years).
This scholarship will cover full-time PhD tuition fees for three years (subject to satisfactory academic performance). This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.
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.
The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £18,000 (tbc) per annum for three years (subject to satisfactory academic performance).
This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.
Due consideration should be given to financing your studies. Further information on cost of living
1.Youssofzadeh, Zanotto, Wong-Lin, Agrawal, Prasad, Agrawal (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
2.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.
3.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.
4.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
5.Friston, FitzGerald, Rigoli, Schwartenbeck & Pezzulo (2017). Active inference: A process theory. Neural Computation, 29(1):1–49, 2017. doi:10.1162/NECO_a_00912
6. Kudithipudi, D., Aguilar-Simon, M., Babb, J. et al. Biological underpinnings for lifelong learning machines. Nat Mach Intell 4, 196–210 (2022). https://doi.org/10.1038/s42256-022-00452-0
Submission deadline
Monday 27 February 2023
04:00PM
Interview Date
18 April 2023
Preferred student start date
18 September 2023
Telephone
Contact by phone
Email
Contact by email
