Background
The nuclear industry is increasingly seeking to deploy robotic and autonomous systems were possible to reduce worker exposure to hazardous environments, improve productivity and enhance operational resilience. Whilst significant progress has been made in remote operations, a major barrier is the ability for robots and operators to work collaboratively within the same safety envelope.
Current safety approaches generally require physical segregation, exclusion zones or reduced robot operating modes, with, in some cases, complete removal of personnel. This can limit the potential benefits that could be realised with autonomy and prevents effective human-robot coexistence in complex nuclear operations.
The PhD project is aimed at developing technologies, methodologies and assurance frameworks to enable humans and robotic systems to operate safely, efficiently and collaboratively within a shared environment.
Vision
To create a nuclear working environment in which robotic systems and operators can safely coexist and collaborate within the same safety envelope, dynamically adapting to changing conditions whilst maintaining demonstrable safety and regulatory compliance.
Research Challenge
There are some fundamental challenges that need to be considered as part of this work:
• Human behaviour is unpredictable.
• Nuclear environments are dynamic.
• Safety regulations ensure operators must face minimal risk with predictable and repeatable operations.
• Autonomous systems need to be predictable and verifiable.
• Regulatory acceptance requires demonstrable evidence of safety performance.
• The complex design of nuclear facilities make wireless communications difficult.
Potential Research Themes
The PhD will cover one or more of the following themes:
Theme 1: Dynamic Safety Envelope Management - Research mechanisms that allow robotic systems to adapt their behaviours based on human presence, intent and task context.
Theme 2: Human Intent Recognition and Behaviour Prediction - Development of systems capable of understanding and predicting human actions within a shared workspace.
Theme 3: Explainable and Assured Artificial Intelligence - Investigation of AI models suitable for safety-critical nuclear applications.
Theme 4: Digital Twin Enabled Human-Robot and Sensor Collaboration - Development of digital representations of people, robots, assets and environments using sensors and condition monitoring to support safe collaboration.
Theme 5: Multi-sensor Environmental Awareness - Development of sensing and perception technologies supporting safe collaboration.
Theme 6: Nuclear Safety Case and Regulatory Frameworks - Creation of assurance methodologies enabling deployment of collaborative robotics in licensed nuclear facilities.
Theme 7: Intelligent Task Orchestration and Collaborative Autonomy - The development of systems that coordinate work between multiple robots and human operators.
Expected Outcomes
The PhD aims to deliver:
• Novel technologies enabling safe human-robot collaboration
• New safety and assurance methodologies for nuclear robotics
• Frameworks for supporting regulatory acceptance
• Demonstrations operating within representative nuclear environments
• Enhanced workforce safety and reduced radiological exposure for operators
• Increased operational efficiency across nuclear decommissioning, waste management, maintenance and future nuclear facilities
Strategic Impact
The outcomes of the PhD will support the nuclear industry transition from segregated robotic operations towards integrated human-robot teams, establishing the technological and regulatory foundations for the next generation of intelligent nuclear workplaces.
Funding notes:
This position is open to UK fee payers AND international students, and the funding will cover all tuition fees and also provide a stipend to cover living costs during the student’s studies.
The successful applicants should have a first degree classified at 1st (or equivalent) in Robotics and AI or a closely related subject. An MSc in robotics or a related area is also highly desirable.
If you are interested in this position, email Dr Steve Davis (s.davis.2@bham.ac.uk) including a CV and a brief (half page) description of the thematic area(s) you would like to focus on and how it is related to your background and experience.
Do not apply though the University admissions system until instructed to by Dr Davis.
