Moi Hoon Yap

[Home tuition fees (£5,006/year for 2025/26) covered for three years; international students must pay the difference. Standard UKRI stipend (£20,780 for 2025/26) provided for duration of award.] This funded PhD studentship at Manchester Metropolitan University offers an outstanding opportunity to join the Faculty of Science and Engineering’s dynamic doctoral research community. The project is part of the university’s strategic investment in developing future thought leaders and is embedded within the AI, Digital and Cyber-Physical Systems research theme. The overarching goal is to design generalisable, context-aware multimodal learning frameworks for modelling human behaviour across diverse socio-contexts, with applications in healthcare, emotional recognition, and human-machine interaction. As a PhD student, you will investigate advanced AI methods that integrate vision, language, and audio signals to interpret and respond to human actions and emotions. The research will address critical challenges in personalised care and assistive technologies, contributing to innovations that improve quality of life. You will explore multimodal learning and unlearning techniques, focusing on bias mitigation and privacy concerns, and validate your methods through real-world use cases. The project is closely linked to a UKRI-funded initiative, providing opportunities to collaborate with postdoctoral researchers, early-career academics, and established professors. You will curate multimodal datasets, develop robust context-aware learning algorithms, and evaluate their generalisability across different socio-contexts. The research environment is vibrant and supportive, with access to cutting-edge resources and expertise in AI and digital technologies. Funding is available for both Home and International students. Home tuition fees (£5,006 per year for 2025/26) are fully covered for three years, and a standard UKRI stipend (£20,780 for 2025/26) is provided for the duration of the award. International students are eligible but must pay the difference in tuition fees (Band 2 for 2025/26). Applicants should have a first class or upper second undergraduate degree in Computer Science or an MSc in a related field. Strong programming skills and motivation for research are essential, with desirable expertise in computer vision, machine learning, and multimodal data processing. Experience with Python and AI/ML libraries such as PyTorch or TensorFlow is required. Candidates should be enthusiastic about advancing AI for societal impact and able to demonstrate relevant skills and experience. To apply, interested candidates should contact Prof. Moi Hoon Yap for an informal discussion. Applications must be submitted online via the university’s Admissions Portal, including the Doctoral Project Applicant Form, CV, and a covering letter detailing how your skills and experience align with the project’s aims and objectives. Please quote reference SciEng-MHY-2026-27-Multimodal Context-Aware Behaviour. The application deadline is 31 January 2026, with an expected start date in October 2026.

[Home tuition fees (£5,006/year for 2025/26) covered for three years; international students must pay the difference. Standard UKRI stipend (£20,780 for 2025/26) provided for duration of award.] This funded PhD studentship at Manchester Metropolitan University offers an exciting opportunity to join the Faculty of Science and Engineering’s vibrant doctoral research community. The project is part of Manchester Met’s AI, Digital and Cyber-Physical Systems research theme, which is dedicated to improving quality of life through cutting-edge technology and impactful research. The focus of this studentship is on designing generalisable, context-aware multimodal learning frameworks to understand and model human behaviours across diverse socio-contexts. Applications of this research include healthcare, emotional recognition, and human-machine interaction, with the aim of advancing personalised care and assistive technologies. 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. The project objectives include curating multimodal datasets, developing robust context-aware learning methods, designing unlearning techniques to address bias and privacy concerns, and evaluating the generalisability of these methods across different socio-contexts. Validation will be carried out through two use cases, ensuring real-world relevance and impact. Funding is available for both Home and International students. Home tuition fees (£5,006/year for 2025/26) are covered for the three-year duration of the award. International students must pay the difference in tuition fees (Band 2 for 2025/26). All students will receive a standard UKRI stipend, currently set at £20,780 for the academic year 2025/26. This financial support enables students to focus on their research and professional development within a collaborative environment, working alongside postdoctoral researchers, early-career academics, and established professors. Applicants should possess a high-grade undergraduate degree (first class or upper second) in Computer Science or an MSc in a related field. Strong motivation for research, excellent programming skills, and experience in developing computer vision and machine learning algorithms are desirable. Candidates should be enthusiastic about advancing AI for societal impact and have knowledge of programming, computer science, computer vision, machine learning, Python, and relevant AI/ML libraries such as PyTorch and TensorFlow. Demonstrated experience with multimodal data processing (vision, language, audio) and machine learning techniques is expected. To apply, interested candidates should contact Prof. Moi Hoon Yap for an informal discussion. The formal application process involves completing the online application form for a full-time PhD in Computing & Digital Technology, submitting the Doctoral Project Applicant Form, CV, and a covering letter that maps skills and experience to the project’s aims and objectives. These documents should be uploaded via the University’s Admissions Portal. The application deadline is 31 January 2026, with an expected start date in October 2026. Please quote the reference SciEng-MHY-2026-27-Multimodal Context-Aware Behaviour in your application.

Manchester Metropolitan University is advertising a funded PhD in Generalisable Multimodal Learning for Socio-Contextual Human Behaviour Modelling . This project sits at the intersection of computer science , multimodal AI , computer vision , natural language processing , and audio analysis . The research will investigate how vision, language, and audio can be combined to understand human behaviour across different social contexts. Potential application areas include healthcare , emotion recognition , assistive technology , and personalised care . The opportunity is described as part of a dynamic UKRI-funded research team , with access to advanced datasets, specialist research facilities, and interdisciplinary collaboration. The post highlights strong doctoral training and professional development support, making it attractive for candidates interested in building research, communication, and career skills alongside technical expertise. Funding: £21,805 annual stipend for 2026/27 plus Home tuition fees. Home and International applicants may apply, although international students must pay the difference between Home and International tuition fees. Eligibility and fit: The advert is aimed at applicants with a strong passion for AI research and its positive social impact. The post does not list detailed academic prerequisites in the social post itself, so candidates should review the full project page and discuss suitability with the named supervisor before applying. Deadline: 27 July 2026. Expected start: January 2027. How to apply: Use the project link for full details and application guidance. Contact Professor Moi Hoon Yap for an informal discussion before submitting the application through the university process.