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School of Electronic Engineering and Computer Science
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  4. Holistic, Contextual, Robust & Trustworthy Multimodal AI for Human Behavior Understanding & Modeling

Holistic, Contextual, Robust & Trustworthy Multimodal AI for Human Behavior Understanding & Modeling

Supervisor: Dr Dimitrios Kollias (QMUL) & Dr Hazem Abdelkawy (TME)

Project Description

This fully funded PhD studentship, supported by Toyota Motor Europe (TME), will be hosted at the Centre for Multimodal AI and the Multimedia and Vision (MMV) Group at Queen Mary University of London (QMUL), UK. The project will be supervised by Dr. Dimitrios Kollias, Assistant Professor in AI.

This PhD project aims to design and develop novel, holistic, context-aware, robust, and trustworthy multimodal AI and machine/deep learning models for the detection and interpretation of emotional and affective states in real-world, unconstrained settings (i.e., in-the-wild).

Understanding human behavior and affect is a complex task, influenced by a wide range of factors including emotion, cultural background, and situational context. This complexity is further compounded by the presence of noisy or biased data, which can reduce model accuracy, and by the opaque nature of many deep learning models, which lack interpretability. Furthermore, models trained on limited or homogenous datasets often struggle to generalize across diverse populations and environments.

This project will address these challenges by advancing research in the following key areas:

1. Multimodal Integration and Sensing – combining and processing data from diverse modalities to capture richer affective signals.

2. Context Awareness – enabling models to interpret affective states with an understanding of contextual factors.

3. Fairness, Explainability, and Ethics – ensuring models are transparent, unbiased, and ethically aligned.

4. Generative AI and Personalization – leveraging generative models to create adaptive, personalized affective computing systems.

This PhD studentship is fully funded for all candidates and lasts for 3.5 years. The candidate is expected to start on September 2025. The studentship includes:

· A tax-free stipend at the UKRI London rate (~£23,000 for 2025 - 2026)

· Full tuition fee waiver (at Home or Overseas rate)

· Support for research expenses, including conference travel

· Engaging in a collaborative and innovative research environment (industrial collaboration with Toyota Motor Europe and academic mentorship at QMUL)

· The chance to work on cutting-edge research and a high-impact project with potential for real-world deployment and patenting

· Access to advanced computing resources/facilities (GPUs, HPC) & personalized mentorship.

National and international applicants should have:

· A Master's degree (or be close to completion) in Electronic Engineering, Computer Science, or a related discipline

· A First-Class or Distinction degree is highly desirable

· Strong background in AI, machine learning, and/or deep learning

· Experience in Python & deep learning libraries (e.g. PyTorch, TensorFlow)

· Prior research experience or publications in relevant fields are highly desirable

· Excellent problem-solving, communication (both written and verbal), and collaboration skills and a passion for research

· For non-native English speakers: a minimum IELTS score of 6.5 overall, with 6.0 in writing, or equivalent qualification

This PhD project offers a unique opportunity to work in a highly collaborative and interdisciplinary environment. The candidate will be part of a dynamic team at the Centre for Multimodal AI and the Multimedia and Vision (MMV) Group at Queen Mary University of London (QMUL), UK. The project is supported by Toyota Motor Europe (TME), providing an excellent blend of academic and industrial collaboration.

The candidate will have the opportunity to collaborate with experts from various fields, including AI, machine learning, and affective computing. This interdisciplinary approach will enhance the research experience and provide a broader perspective on the project's challenges and solutions. Additionally, the candidate will benefit from the industrial insights and resources provided by TME, ensuring that the research has real-world applications and impact, such as deployment and evaluation within vehicle or robotics testing environments.

For more information about the project or for some initial discussions, please contact Dr Dimitrios Kollias; in the email also include your CV.

Supervisor Dr Dimitrios Kollias (he/him):

  • Member of Centre for Multimodal AI Affiliate Member of Centre for Human Centred Computing
  • Member of Multimedia and Vision Group
  • Member of Queen Mary Computer Vision Group
  • Associate Member of Centre for Advanced Robotics Academic Fellow of Digital Environment Research Institute

How to apply

Queen Mary is interested in developing the next generation of outstanding researchers and decided to invest in specific research areas. For further information about potential PhD projects and supervi-sors please see the list of the projects at the end of this page.

Applicants should work with their prospective supervisor and submit their application following the instructions at: http://eecs.qmul.ac.uk/phd/how-to-apply/

The application should include the following:

· CV (max 2 pages)

· Cover letter (max 4,500 characters) stating clearly in the first page whether you are eligible for a scholarship as a UK resident (https://epsrc.ukri.org/skills/students/guidance-on-epsrc-stu-dentships/eligibility)

· Research proposal (max 500 words)

· 2 References

· Certificate of English Language (for students whose first language is not English)

· Other Certificates

Please note that to qualify as a home student for the purpose of the scholarships, a student must have no restrictions on how long they can stay in the UK and have been ordinarily resident in the UK for at least 3 years prior to the start of the studentship. For more information please see: (https://epsrc.ukri.org/skills/students/guidance-on-epsrc-studentships/eligibility)

Application Deadline

The deadline for applications is the 18th June 2025.

For general enquiries contact Mrs Melissa Yeo at m.yeo@qmul.ac.uk (administrative enquiries) or Dr Arkaitz Zubiaga at a.zubiaga@qmul.ac.uk (academic enquiries) with the subject “EECS 2025 PhD schol-arships enquiry”. For specific enquiries contact Dr Dimitrios Kollias at d.kollias@qmul.ac.uk

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