Morden machine learning tasks such as multi-modal learning, multi-view learning, domain adaptation/transfer learning, zero/few-shot learning, human-in-the-loop modelling, etc. share similar modelling strategies and frameworks. They could be unified as a process of learning from multiple information sources aiming at predictions based on input obtained from different but relevant data sources. On the other hand, there are high noise, missing entries, heterogeneousness, and misalignment (or called gap, shift) between the learned representations/distributions across data sources etc. in these learning tasks. Therefore, how to systematically integrate these uncertainties and heterogeneity while discovering their similarities across tasks become crucial in the feature learning. This project aims at establishing unified theories and frameworks for algorithm development to optimise data usage in hidden representation spaces and improve learning efficiency. Through theory and algorithm unification under the general problem formulation of learning from multi-source data, all the aforementioned learning tasks will be benefited.
Eligibility
Applicants must have obtained or be about to obtain a First or Upper Second class UK honours degree, or the equivalent qualifications gained outside the UK, in an appropriate area of science, engineering or technology.
Before you Apply
Applicants must make direct contact with preferred University of Manchester supervisors before applying. It is your responsibility to make arrangements to meet with potential supervisors, prior to submitting a formal online application.
How to apply:
You will need to submit an online application through our website here: https://uom.link/pgr-apply
When you apply, you will be asked to upload the following supporting documents:
• Final Transcript and certificates of all awarded university level qualifications
• Interim Transcript of any university level qualifications in progress
• CV
• You will be asked to supply contact details for two referees on the application form (please make sure that the contact email you provide is an official university/ work email address as we may need to verify the reference)
• English Language certificate (if applicable)
Your application form must be accompanied by a number of supporting documents by the advertised deadlines. Without all the required documents submitted at the time of application, your application will not be processed and we cannot accept responsibility for late or missed deadlines. Incomplete applications will not be considered. If you have any queries regarding making an application please contact our admissions team [Email Address Removed]
Equality, Diversity and Inclusion
Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. We know that diversity strengthens our research community, leading to enhanced research creativity, productivity and quality, and societal and economic impact. We actively encourage applicants from diverse career paths and backgrounds and from all sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation and transgender status.
We also support applications from those returning from a career break or other roles. We consider offering flexible study arrangements (including part-time: 50%, 60% or 80%, depending on the project/funder).
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