Continual learning for modelling non-stationary systems

Summary

Real-world environments are often non-stationary. Machine learning systems such as classifiers used for pattern recognition often make the stationarity assumption in input data distribution density during training and operation phases. As a result, their 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 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.

Essential criteria

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.

• Experience using research methods or other approaches relevant to the subject domain
• A demonstrable interest in the research area associated with the studentship

Desirable Criteria

If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.

• First Class Honours (1st) Degree
• Masters at 70%
• For VCRS Awards, Masters at 75%
• Experience using research methods or other approaches relevant to the subject domain
• Work experience relevant to the proposed project
• Publications - peer-reviewed
• Experience of presentation of research findings

Equal Opportunities

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.

Funding and eligibility

This project is funded by:

• Department for the Economy (DfE)
• Vice Chancellor's Research Scholarship (VCRS)

Our fully funded PhD scholarships will cover tuition fees and provide a maintenance allowance of £19,237 (tbc) per annum for three years* (subject to satisfactory academic performance).  A Research Training Support Grant (RTSG) of £900 per annum is also available.

These scholarships, funded via the Department for the Economy (DfE) and the Vice Chancellor’s Research Scholarships (VCRS), are open to applicants worldwide, regardless of residency or domicile.

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 time PhD scholarships may be available, based on 0.5 of the full time rate, and will require a six year registration period (individual project advertisements will note where part time options apply).

Due consideration should be given to financing your studies.