Data-adaptive sequential inference via differentiable particle filters

This project explores the intersection between machine learning and sequential Monte Carlo methods for data-adaptive sequential inference tasks with accurate uncertainty prediction.

Start date

1 October 2023


36 months

Application deadline

Funding source

University of Surrey

Funding information

Full tuition fee cover, stipend of c £17,000 p.a. and a £3,000 Research Training Support Grant.


Sequential inference is a key task in many of the everyday artificial intelligence (AI) and data science applications such as target tracking. Particle filters are a family of flexible algorithms using Bayesian Monte Carlo methods for sequential inference, by sequentially updating the probability distribution of internal states of a dynamic system from noisy measurements.

This project will leverage artificial neural networks to automatically build various components of particle filters. Replacing heuristic models in particle filters with data-driven ones would make them an extremely powerful tool in a wide range of real-world applications ranging from robotics and financial modelling.


Additional notes

Please note that this studentship award is part of a wider studentship competition. Those successful in being shortlisted will be put forward to a central panel consisting of University of Surrey and University of Wollongong staff who will then assess the applications and select four of the nominated candidates for funding.

Eligibility criteria

Open to UK and international candidates.

A Bachelor’s degree or above in computer science, electrical engineering, statistics, mathematics, physics or similar (a first class or the equivalent from an overseas university).

Past research experience with prior publications is preferable although not essential.

IELTS minimum 6.5 overall with minimum 6.0 in each component, or equivalent.

How to apply

Applications should be submitted via the Computer Science PhD programme page on the "Apply" tab. Please clearly state the studentship title and supervisor on your application. In place of a research proposal you should upload a document stating the title of the project that you wish to apply for, the name of the relevant supervisor and a personal statement. The statement should explain how your previous experience has prepared you for doctoral research and this project in particular. Explain how this PhD will support your career aspirations (maximum 500 words).

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Application deadline

Contact details

Yunpeng Li
14 BB 02
Telephone: +44 (0)1483 682626

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