Publisher
source

University College London

Fully Funded PhD Studentship in Foundational AI, Reinforcement Learning, and Sequential Decision-Making University College London in United Kingdom

Degree Level

PhD

Field of study

Computer Science

Funding

Fully funded 4-year PhD studentship. Home tuition fees are covered plus a living-cost stipend expected to be £23,805 in 2026/27, increasing annually.

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Country

United Kingdom

University

University College London

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Keywords

Computer Science
Electrical Engineering
Information Technology
Mathematics
Artificial Intelligence
Reinforcement Learning
Generative Modeling
Scientific Discovery
ML

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About this position

UCL Electronic & Electrical Engineering is recruiting a fully funded 4-year PhD student to work on Foundational AI.

The project sits at the intersection of artificial intelligence, machine learning, reinforcement learning, sequential decision-making, generative models, and foundation models, with possible applications in communication systems, engineering, and scientific discovery.

Supervision is by Dr Sattar Vakili at University College London. The research will combine rigorous mathematical development with computational experiments, and the exact topic will be shaped jointly with the successful candidate.

Funding includes home tuition fees plus a living-cost stipend expected to be £23,805 in 2026/27, increasing annually. The opportunity is stated as being for eligible UK applicants.

Applications are open until a suitable candidate is found. Interested applicants should review the full details via the application link and contact Dr Sattar Vakili for informal queries.

Funding details

Fully funded 4-year PhD studentship. Home tuition fees are covered plus a living-cost stipend expected to be £23,805 in 2026/27, increasing annually.

What's required

Applicants should be eligible UK applicants. The studentship is for a PhD and the research is expected to combine rigorous mathematical development with computational experiments and applications. Interest in AI, machine learning, reinforcement learning, sequential decision-making, generative and foundation models, and related applications is relevant.

How to apply

Read the full details at the application link and follow the instructions there. Contact Dr Sattar Vakili for informal queries before applying. Applications remain open until a suitable candidate is found.

More information can be found here

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