University of Liverpool PhD Studentship

PhD opportunity at the University of Liverpool in the School of Computer Science and Informatics, focused on computational complexity theory for neuromorphic computing and spiking neural networks.

This theoretical project sits at the intersection of computer science, mathematics, and emerging neuromorphic systems. The research aims to develop a formal complexity framework for non-von Neumann architectures, with attention to resource measures such as time, space, communication cost, latency, and energy. The project is undertaken with Dr David Purser and Dr John Sylvester in collaboration with Dstl (Defence Science and Technology Laboratory).

Funding is strong: the studentship is fully funded for home students only, covering home tuition fees, an enhanced tax-free stipend above UKRI rates, travel funding for conferences or funder visits, and support for a laptop. The stipend is listed at approximately £23,450 in year 1, £24,350 in year 2, £25,300 in year 3, and £26,400 pro-rata in year 4.

Eligibility highlights: applicants should have a strong background in mathematics and/or computer science, ideally with theoretical foundations. A master’s degree in a related area is desirable, especially with coursework in computational complexity, algorithms, logic, probability, or theoretical computer science. Exceptional candidates with a First Class bachelor’s degree or relevant experience may also be considered. Due to the collaboration with a UK government organisation, eligibility is restricted to UK nationals.

How to apply: use the University of Liverpool postgraduate research application form for a PhD in Computer Science, include the project title and reference number CSPR001, and upload the required documents (personal statement, transcripts, degree certificates, passport details, English language certificates if applicable, CV, and two referees). Applications may close early once a suitable candidate is found.

Research keywords: computational complexity theory, neuromorphic computing, spiking neural networks, theoretical computer science, algorithms, logic, probability, edge computing, AI, energy-efficient computing.