This project lies on the intersection of modern machine learning techniques and sequential Monte Carlo methods. Sequential Monte Carlo (SMC) methods, e.g. particle filters, are a class of powerful simulation-based algorithms to utilise data uncertainty and generate model uncertainty. Replacing the heuristic models in SMC by data-driven ones through the incorporation of machine learning will make them an extremely powerful tool in real-world applications including computer vision, finance, object tracking and robotics. This project will develop innovate statistical methods and apply the developed techniques in large-scale real-world datasets.
Supervisors: Dr Yunpeng Li, Professor Wenwu Wang.
This project is open to UK and international students. The application is rolling-based with no fixed submission deadline until the position is filled. Early applications are strongly encouraged for early PhD start.
The PhD student will be based at the Nature Inspired Computing and Engineering (NICE) research group in the Department of Computer Science at the University of Surrey. The student will also benefit from ample computing and research resources from the Centre for Vision, Speech and Signal Processing (CVSSP) and the Surrey Institute for People-Centred AI.
More about this project.
Entry requirements
A Bachelor’s degree or above in Computer Science, Electrical Engineering, Statistics, Mathematics, Physics or similar (a First Class or good Upper Second Class Honours degree, or the equivalent from an overseas university).
English language requirements: IELTS Academic 6.5 or above (or equivalent) with 6.0 in each individual category, or equivalent. More about our English language requirements.
How to apply
Applications should be submitted via the Computer Science PhD programme page on the "Apply" tab.
In place of a research proposal you should upload a document stating the title of the project that you wish to apply for and the name of the relevant supervisor.
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