Closing date: 1 July 2027
Funding: Competition Funded PhD Project (Students Worldwide)
About the Project
Supervisors: Dr Rafael Mestre, Prof Stuart E. Middleton
This project will explore how multimodal large language models (LLMs) can understand arguments across text, audio, images, and video. You'll develop methods using LLMs, vision-language models (VLMs), multimodal reasoning, and argument mining to identify claims, evidence, and relationships.
Multimodal LLMs can process text, speech, images, and video, but still struggle to understand how claims, evidence, and counterarguments connect across these different forms of communication. Addressing this problem requires models that can identify argumentative structures, integrate information across modalities, reason with this information and explain what evidence supports their conclusions.
In this project, you'll develop and evaluate new methods for multimodal argument understanding. Depending on your interests and experience, you could investigate:
- large language models and multimodal reasoning for identifying claims, premises, evidence, and argumentative relationships
- connecting spoken or written claims with visual evidence
- analysing arguments across long complex discussions
Possible methods include multimodal reasoning, multimodal CoT distillation, multimodal retrieval, structured prediction, retrieval-augmented generation, and combinations of neural and symbolic approaches.
You'll have flexibility to shape precise methods, modalities, and applications. Potential directions include:
- developing new datasets and evaluation methods
- improving cross-domain generalisation
- testing whether models use multimodal evidence faithfully
- designing more interpretable and reliable systems
Applications might include online discussion, political debate, public deliberation, or other forms of written, spoken, and visual communication.
The project offers the opportunity to contribute across natural language processing, multimodal machine learning, computer vision, and audio analysis. You'll develop a coherent technical contribution around your strengths while gaining experience of interdisciplinary research and the wider implications of AI for real-world communication.
You'll join the School of Electronics and Computer Science. You'll have access to professional-development opportunities that may include teaching experience, engagement with networks such as the Alan Turing Institute and Responsible AI UK programme, and support from Future Worlds to explore the commercial potential of your research.
Entry requirements
A UK 2:1 honours degree, or its international equivalent, in one of the following:
- computer science
- artificial intelligence
- data science
- a closely related quantitative discipline
Practical programming experience in Python is essential.
Applicants are strongly encouraged to have a first-author publication in peer-reviewed natural language processing, machine learning, or a closely related area.
Experience with machine learning or natural language processing through a dissertation, research project, employment, or substantial independent work is desirable.
Experience with deep learning, large language models, audio, computer vision, or multimodal data would be helpful but is not required. No previous background in argument mining is expected.
Due to funding limits, only UK and EU-based candidates are invited to apply.
Fees and funding
We offer a range of funding opportunities for both UK and international students. Horizon Europe fee waivers automatically cover the difference between overseas and UK fees for qualifying students.
Competition-based Presidential Bursaries from the University cover the difference between overseas and UK fees for top-ranked applicants.
Competition-based studentships offered by our schools typically cover UK-level tuition fees and a stipend for living costs for top-ranked applicants.
Funding will be awarded on a rolling basis, so apply early for the best opportunity to be considered.
For more information, please visit our postgraduate research funding pages.
How to apply
You need to:
- choose programme type (Research), 2027/28, Faculty of Engineering and Physical Sciences
- select Full time or Part time
- search for programme PhD Computer Science (7089)
- add name of the supervisor in section 2 of the application
Applications should include:
- research proposal
- your CV (resumé)
- 2 academic references
- degree transcripts and certificates to date
- English language qualification (if applicable)
Contact us
Faculty of Engineering and Physical Sciences
For questions about applying, doctoralcollege-admissions@soton.ac.uk.
Project leader
For an initial conversation, R.Mestre@soton.ac.uk
The School of Electronics and Computer Science is committed to promoting equality, diversity inclusivity as demonstrated by our Athena SWAN award. We welcome all applicants regardless of their gender, ethnicity, disability, sexual orientation or age, and will give full consideration to applicants seeking flexible working patterns and those who have taken a career break. The University has a generous maternity policy, onsite childcare facilities, and offers a range of benefits to help ensure employees’ well-being and work-life balance. The University of Southampton is committed to sustainability and has been awarded the Platinum EcoAward.