Fully Funded PhD in Continual Agent Learning for Lifelong Adaptation and Improvement in Complex Environments Queen Mary University of London in United Kingdom

Queen Mary University of London is advertising a fully funded PhD studentship in Continual Agent Learning for Lifelong Adaptation and Improvement in Complex Environments within the School of Electronic Engineering and Computer Science.

The project sits at the intersection of Computer Science, Artificial Intelligence, Machine Learning, Reinforcement Learning, and large language models, with a strong emphasis on AI agents, multimodal learning, catastrophic forgetting, long-horizon planning, and knowledge transfer. Research directions include language-based continual agents, multimodal agents, memory and retrieval, forward/backward transfer, and new benchmarks for lifelong learning agents.

The supervisor is Dr Diana Benavides-Prado (Queen Mary University of London). The post also references the Centre for Multimodal AI and related academic profile pages.

This opportunity is aimed at home/UK candidates only. Funding includes home-rate tuition fees plus a London stipend at QMUL rates, currently around £22,618 per year. The studentship lasts 3 years.

Applicants should have a background in Computer Science, AI, Data Science, Mathematics, or Statistics, with strong Python and PyTorch skills and a solid machine learning foundation. Experience with foundation models and agent frameworks is desirable. Research experience such as an MSc thesis, publications, preprints, or open-source ML work is an advantage.

The deadline for applications is 2026-09-30. Interviews are expected in early October, and the successful candidate is due to start in January 2027. Applications should be submitted via Queen Mary’s PhD application instructions page, with a CV, cover letter, research proposal, two references, and supporting certificates.