Artificial Intelligence (AI) systems are prevalent in our daily life. Yet, challenges remain for their performance assurance due to the high dimensionality/scale of those systems (e.g., many options to tune), expensive measurements (e.g., taking hours/days to train), and the uncertainty on the operating environment (e.g., poor generalisation). This project seeks to investigate a self-adaptive approach that intelligently optimises/tests the performance (e.g., speed, accuracy, and resource computation) of AI systems at various levels and granularities, considering the fidelity, uncertain constraints, and the environments under which they will operate. The ultimate goal is to equip current AI systems with the ability of “cognitive self-adaptation”, so that they behave, just like human, to consistently achieve the best of their performance in daily work. To that end, the student will investigate novel approaches, such that the characteristics and expert knowledge of the domain can be fully exploited, taking some domain-specific “simplified shortcuts” to improve the result with less overhead. The student will evaluate the approach on real-world open-source AI systems/frameworks that are widely used.
Primary Supervisor: Dr T Chen
[Email Address Removed]
Secondary Supervisor: Dr Lin Guan
[Email Address Removed]
Entry requirements for United Kingdom
Applicants should have, or expect to achieve, at least a 2:1 Honours degree (or equivalent) in Computer Science, Software Engineering, Artificial Intelligence or a related subject. A relevant Master’s degree and/or experience in one or more of the following will be an advantage: performance engineering, machine learning, metaheuristics algorithms. You will also be hardworking, a good communicator, good at working as part of a team, can bring new perspectives and learn new knowledge. We welcome applications from under-represented groups.
English language requirements
Applicants must meet the minimum English language requirements. Further details are available on the International website.
Find out more about research degree funding
How to apply
All applications should be made online. Under programme name, select Computer Science. Please quote the advertised reference number: SCI23-TC in your application. See studentship assessment criteria.
To avoid delays in processing your application, please ensure that you submit the minimum supporting documents.