"PhD Studentship - Generalisable Multimodal Learning for Socio-Contextual Human Behaviour Modelling"

Manchester Metropolitan University

Qualification Type:PhDLocation:ManchesterFunding for:UK Students, EU Students, International StudentsFunding amount:Not SpecifiedHours:Full TimePlaced On:4th December 2025Closes:31st January 2026Reference:SciEng-MHY-2026-27-Multimodal Context-Aware Behavi

Project advert

This funded PhD studentship is part of Manchester Met’s strategic investment in developing the next generation of thought leaders. It offers an exciting opportunity to join the Faculty of Science and Engineering’s vibrant doctoral research community, committed to delivering excellent research with real-world impact.

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.

Project aims and objectives

Aim: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 £5,006 for the year 2025/26. Eligible international students will need to make up the difference in tuition fee funding (Band 2 for the year 2025/26).

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 £20,780 for the academic year 2025/26.

Specific requirements of the candidate

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 should 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 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: 31st January 2026

Expected start date: October 2026

Please quote the reference: SciEng-MHY-2026-27-Multimodal Context-Aware Behaviour