PhD in Computer Science: PhD Position in Edge AI (PhD)

The University of Liverpool, founded in 1882 and located in the vibrant city of Liverpool, is a research-intensive institution with a global student body of around 32,000, including approximately 17,700 international students. The university offers a wide range of programs—over 200—across sciences, engineering, humanities, health and business, combining rigorous academics with practical industry links. Its historic campus and city connections provide a lively cultural setting and strong transport links across the UK and beyond.

Research and employability are central to the Liverpool experience, with interdisciplinary projects, industry partnerships and extensive placement opportunities embedded in many degrees. Students benefit from modern facilities, dedicated career services and a supportive academic community that helps translate theory into professional practice. The university's global networks also create exchange and collaboration pathways for study abroad and joint research.

International students receive comprehensive support from pre-arrival guidance to on-campus services, including English language support, visa advice and student societies that foster social integration. Liverpool’s affordable living costs relative to other UK cities, combined with its cultural life and sporting traditions, make it attractive to students seeking a balanced academic and social experience. The University of Liverpool is well-suited to ambitious students aiming for impactful research, strong career outcomes and a cosmopolitan student life.

Overview

This PhD position is focused on the development of edge AI methods for wearable surface electromyography (sEMG) sensing in industrial human-centred applications. The PhD candidate will investigate how real-time AI models can be designed, optimised, and deployed on resource-constrained wearable and embedded platforms for interpreting sEMG signals.

About this opportunity

The research will involve real-time sEMG signal processing, hand gesture recognition, lightweight neural network design, model compression, sensor validation, data collection, and embedded deployment. The candidate will work with wearable sensing systems and adaptive human–machine interfaces, with the aim of developing robust, low-latency, and reliable edge AI solutions suitable for real-world operational environments.