About the Project
The operating theatre is a complex, high-pressure environment where effective teamwork, communication are critical for patient safety and procedural efficiency. Increasing surgical demand, the utilisation of surgical robotics, procedural complexity, and workflow pressures present significant challenges for healthcare services. Recent advances in artificial intelligence, computer vision, humanoid robotics, and large language models create opportunities for intelligent robotic systems to…
potentially support surgical teams and enhance surgical workflows.
This PhD project aims to investigate the feasibility for the use of humanoid robots in the surgical environment to support surgical staff. It will explore AI driven data analysis and predictive support in and outside of the surgical theatre. Core themes potentially include AI-driven planning for distributed decision-making, image processing to support rapid perception and situational awareness, and the deployment of compact, local large language models (LLM’s) to facilitate effective human–robot interaction negating the need and reliance on cloud connectivity. By integrating these capabilities, the project aims to create scalable and interoperable frameworks that support robust collaboration across heterogeneous robotic agents and people. Ultimately, the research will contribute to both theoretical and practical advances providing a foundation for next-generation robot collectives that can operate intelligently, safely and cooperatively in surgical real-world environments.
Supervisors
- Professor Philip Breedon
- Associate Professor Pedro Machado
- Dr Kartikeya Walia
- Aron Kisdi
Entry qualifications
Applicants should hold either a first-class honours undergraduate degree or a master’s degree with Merit or Distinction in a relevant science, design, computing, or engineering discipline. Applications are particularly encouraged from graduates in Mechanical Engineering, Electrical and Electronic Engineering, Computer Science or closely related disciplines. Candidates should demonstrate strong analytical and problem-solving skills, with an interest and practical knowledge of robotics systems, AI, digital technologies, data-driven decision making, and the development of innovative engineering solutions.
