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University of Cambridge
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6 days ago
PhD Studentship in Monitoring and Increasing LLM Safety University of Cambridge in United Kingdom
Degree Level
PhD
Field of study
Computer Science
Funding
Full funding availableDeadline
Jul 30, 2026
Country
United Kingdom
University
University of Cambridge

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About this position
PhD Studentship in Monitoring and Increasing LLM Safety at the University of Cambridge.
This fully funded studentship supports research on making large language models safer, with a focus on evaluating model behaviour, monitoring outputs at inference time, and reducing risks from LLM deployment. The project is grounded in AI safety and explores approaches such as white-box mechanistic interpretability and black-box behavioural research.
Early work in the first 1.5 years will concentrate on increasing chain-of-thought faithfulness and mitigating encoded reasoning. Two possible research directions are outlined: one investigates whether CoT is used in a straightforward way by applying perturbation methods such as paraphrasing intermediate outputs and then examining performance changes; the other aims to train for transparency by using a human or AI predictor to assess whether model outputs can be predicted from the CoT, with predictor accuracy used as a reward signal during training.
The award is fully funded and covers fees and maintenance for either a home or overseas candidate. Funding is provided by Coefficient Giving, and the project is aligned with their focus on encoded reasoning in CoT and inter-model communication. After the initial scoped projects are completed, the student, supervisor, and funder will decide on the next research direction.
Applicants should have, or expect to obtain by the start date, at least a first degree in Engineering or a related subject. Experience in software development projects or research on LLMs is desirable. Candidates must also secure a place on the PhD programme through separate Graduate Admissions application to the University of Cambridge. The funding application is submitted via a Google Form with a two-page CV and research proposal, and applications are reviewed on a rolling basis. The postgraduate admissions deadlines noted in the advert are 14 May for October start and 30 July for January start, though earlier application is recommended.
This opportunity is a strong fit for students interested in machine learning safety, interpretability, reasoning transparency, and empirical evaluation of advanced AI systems.
Funding details
Full funding including tuition fees and living expenses is available for this position. The scholarship covers all educational costs and provides a monthly stipend.
How to apply
Please submit your application including a cover letter, CV, academic transcripts, and contact information for two references. Applications should be sent via the online portal before the deadline.
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