PhD Studentship: Predictive Network Intelligence for Medical Telerobotics

Robotic-assisted surgery is rapidly expanding in the UK, with over 100,000 procedures performed in 2024 alone. Emerging 5G and future 6G networks are making remote surgery increasingly feasible, allowing surgeons to operate on patients from different locations. However, communication delay remains a major safety challenge. Even small or unexpected delays can affect robot responsiveness and compromise surgical precision.

This is further complicated by the fact that current Internet infrastructure provides best-effort service rather than guaranteed real-time performance, while telesurgery requires reliable transmission of demanding multi-modal data such as haptic feedback, video, and 3D sensing data.

This project will develop AI-driven predictive network intelligence to anticipate delay and network instability before they occur. By analysing historical and real-time network data, the system will provide early warning and enable adaptive robotic control, such as adjusting control sensitivity or restricting high-risk movements during poor network conditions. This predictive capability will help maintain safe and stable human–robot interaction. This PhD project, delivered in collaboration with clinical experts at University Hospitals Coventry & Warwickshire/NHS Trust. The research will involve emulating laparoscopic surgical tasks using a robotic platform under varying network conditions. Machine learning and time-series modelling techniques will be used to predict delay and performance degradation. The predictive models will be integrated into a real-time robotic system and evaluated in realistic scenarios, including input from clinical collaborators, to assess improvements in safety and operational reliability. This project offers a unique opportunity to work at the intersection of robotics, AI, communication networks, and healthcare, contributing to the development of intelligent and resilient systems that can enable safe remote surgery and improve access to specialist care.

Funding 

The scholarship will cover full tuition fees at the UK/Home fee level and will also provide an annual stipend for 3.5 years at the UKRI rate.

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