Encouraged by the continuing success of modern machine learning (ML) techniques, researchers have become ambitious to develop ML solutions for challenging science and engineering problems with complex input.

For instance, in physics-informed ML, in addition to data examples used by a standard ML setup, domain knowledge serves as an additional input. It can be in an explicit form of rigorous physical laws, or an implicit form of extra data examples collected from physical simulations or their ML surrogates. In medical domains, patient data is typically distributed across multiple hospitals, multi-source learning is used to integrate diverse patient populations to build robust models, but having to protect sensitive information.

Various modern ML paradigms are proposed to address the diverse input needs, accompanied by a boost in algorithmic development, e.g., multi-modal learning, transfer learning, federate learning, and knowledge embedding, etc. However, a significant motivation of applying ML techniques in science and engineering is to accelerate knowledge discovery. It is very much not convenient and time consuming for a user to dive into the ML ocean, searching new techniques to accommodate their own particular input setup and deciding the best modelling practice.

This PhD project will aim at automatic solution development, supporting flexible input setups and addressing in one modelling framework multiple ML tasks as mentioned above, to ease the development burden from users. It will research unified and modular modelling strategies, capable of optimally fusing and aligning diverse types of relevant information sources in representation spaces at both data and model levels, preferably with theoretical guarantees. It will consider variability in data quality, heterogeneity in data sources, information (mis)alignment, domain shift, missing data modality, data privacy, data and computing cost. It will focus on targeted scientific problems to test the solutions, aiming at a lowest development cost and highest solution quality.

Before you apply: We strongly recommend that you contact the supervisor(s) for this project before you apply.

How to apply: To be considered for this project you’ll need to complete a formal application through our online application portal. This link should directly open an application for FSE Bicentenary PhD . Please select University of Manchester funding in the funding section of the form.

When applying, you’ll need to specify the full name of this project , the name of your proposed supervisor/s , details of your previous study, and names and contact details of two referees . You are also required to upload your CV and a Personal Statement describing your motivation for applying for the project.

Your application cannot be processed without all of the required documents, and we cannot accept responsibility for late or missed deadlines where applications are incomplete.

Equality, diversity and inclusion: Equality, diversity and inclusion are fundamental to the success of The University of Manchester, and are 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).

Eligibility

Applicants should have, or expect to achieve, at least a 2.1 honours degree or a master’s (or equivalent) in a relevant science or engineering related discipline.

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