PhD Studentship: Examining the Female High-Performance Environment in Aquatics GB: A Qualitative, Co-Produced Study

Project advert

This studentship is part of Manchester Met’s strategic investment in developing the next generation of thought leaders.

This project sits within Manchester Met’s AI, Digital and Cyber-Physical Systems research theme, focused on improving quality of life through cutting-edge technology. The research aims to design context-aware multimodal AI methods to understand and model human behaviours across diverse socio-contexts. Applications include healthcare and emotional recognition, addressing critical challenges in human-machine interaction and personalised care.

The successful candidate will explore multimodal learning and unlearning techniques using vision, language, and audio signals to build intelligent systems capable of interpreting and responding to human actions and emotions. This work will contribute to innovations in assistive technologies and healthcare solutions.

You will have the opportunity to work closely with the UKRI-funded project, collaborating with a dynamic team of postdoctoral researchers, early-career academics, and established professors. This partnership provides access to cutting-edge datasets, advanced methodologies, and a strong interdisciplinary network, enhancing the impact and scope of your research.

Project aims and objectives

The project aims to design context-aware multimodal learning frameworks to effectively understand and model human behaviours across diverse socio-contexts.

Objectives:

• Identify and curate multimodal datasets representing varied socio-contexts.
• Develop robust context-aware multimodal learning methods.
• Design and implement multimodal unlearning techniques to address bias and privacy concerns.
• Evaluate the generalisability of multimodal learning across different socio-contexts.
• Validate the proposed methods through two use cases.

Funding

Both Home and International students can apply. Home tuition fees will be covered for the duration of the three years award, which is £5,238 for the year 2026/27. Eligible international students will need to make up the difference in tuition fee funding (Band 2 for the year 2026/27).

The student will receive a standard stipend payment for the duration of the award. These payments are set at a level determined by the UKRI, currently £21,805 for the academic year 2026/27

Specific requirements of the candidate

The qualifications, skills, knowledge and experience applicants should have for this project, in addition to our standard entry requirements.

Candidates must have a strong motivation for research and excellent programming skills. Expertise in developing computer vision and machine learning algorithms would be desirable, highly motivated and enthusiastic about advancing AI for societal impact.

Qualifications

• A high-grade undergraduate degree (first class or upper second) in Computer Science or MSc in related field.

Skills

• Knowledge of programming, Computer Science, Computer Vision, Machine Learning, or related discipline.
• Experience with Python and relevant AI/ML libraries (e.g., PyTorch, TensorFlow).
• Demonstrated knowledge of multimodal data processing (e.g., vision, language, audio) and machine learning techniques.

How to apply

Interested applicants may contact Prof. Moi Hoon Yap for an informal discussion. 

To apply you will need to complete the online application form for a full time PhD in Computing & Digital Technology

Please amend/delete the following according to your requirements.

Please complete the Doctoral Project Applicant Form, and include your CV and a covering letter to demonstrate how your skills and experience map to the aims and objectives of the project, the area of research and why you see this area as being of importance and interest. 

Please upload these documents in the supporting documents section of the University’s Admissions Portal.

Applications closing date: 27 July 2026 

Expected start date: January 2027

Please quote the reference: SciEng-MHY-2026-27- Multimodal Learning

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