Imperial College London

About the role

Are you a quantitative researcher interested in the foundations of AI safety and reliability? We are seeking a Postdoctoral Research Associate to join PRISM (Probabilistic Rare-event Inference for Safety of Models), a new research programme developing rigorous statistical methods to quantify extremely rare but high-impact failures in large language models and other generative AI systems. Working in the Department of Mathematics at Imperial College London, you will be at the forefront of transforming AI safety evaluation into a reproducible, auditable science grounded in probability, statistics, and large-scale computation.

What you would be doing

In this role, you will play a central part in the design, implementation and validation of new probabilistic methodologies for AI safety assurance. Your work will combine theory, algorithms and computation, with clear pathways to real-world impact.

Specifically, you will:

• Develop and analyse rare-event simulation and Sequential Monte Carlo methods tailored to generative AI systems.
• Design and run large-scale computational experiments to estimate extremely low-probability unsafe behaviours in language models.
• Contribute to the development of an end-to-end research pipeline, integrating prompt generation, stochastic rollouts, verification, and uncertainty-quantified risk estimates.
• Produce high-quality research outputs, including peer-reviewed publications, open-source software, and technical reports.
• Collaborate closely with academic colleagues, research software engineers, and external partners working at the interface of statistics, machine learning and AI governance.
• Present your findings at seminars, workshops and international conferences, and contribute to the wider intellectual life of the research group.

What we are looking for

You will be a motivated and intellectually curious researcher with strong quantitative foundations and an interest in challenging, open-ended problems.

We are particularly interested in candidates who have:

• A PhD (or near completion) in Statistics, Machine Learning, Applied Mathematics, Computer Science, or a closely related discipline.
• A strong background in probability, statistics, or statistical inference.
• Experience conducting independent research and producing high-quality outputs (e.g. publications, preprints, software).
• Strong programming and computational skills, for example in Python and scientific computing or machine-learning frameworks.
• The ability to communicate complex technical ideas clearly, both in writing and verbally.
• A collaborative mindset and the ability to organise your own work effectively.

Experience with Monte Carlo methods, generative models, or AI evaluation is desirable but not essential.

What we can offer you

• The opportunity to work on a high-profile, methodologically ambitious research programme at the forefront of AI safety and statistical science.
• The chance to develop foundational research with relevance to industry, regulation and public policy.
• The opportunity to continue your career at a world-leading institution and be part of our mission to continue science for humanity.
• Grow your career: gain access to Imperial’s sector-leading dedicated career support for researchers as well as opportunities for promotion and progression.
• Sector-leading salary and remuneration package (including 41 days off a year and generous pension schemes).
• Be part of a diverse, inclusive and collaborative work culture with various staff networks and resources to support your personal and professional wellbeing.

Further information

This is a full-time post (35 hours per week).

This role is for a fixed-term contract for 24 months.

(Salary offered will be commensurate with skills and experience.)

If you require any further details about the role, please contact: Andrew Duncan– [a.duncan@imperial.ac.uk]

Available documents

Attached documents are available under links. Clicking a document link will initialize its download.

• download: Postdoctoral Research Associate Imperial Job Description.pdf
• download: Employee Benefits Booklet.pdf