University of Nottingham · Faculty of Engineering
PhD project: Resilient Processes of Nuclear Fusion Plant Maintenance and Decommissioning using Robotic Technologies
Nottingham
Fixed-term
Full-time
Funding to be sourced by applicant — see university funding page
Closes 28 Dec 2026
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Sign in Create an accountSupervised by Rasa Remenyte-Prescott (Faculty of Engineering)
Aim: To develop a modelling approach for nuclear fusion plant maintenance and decommissioning processes.
More than 80% of the world's energy still comes from fossil fuels. Climate change and diminishing fuel reserves mean that it is becoming essential to find alternative, sustainable technologies to supply a growing global population. With no greenhouse gas emissions, inherent safety features and virtually limitless fuels, nuclear fusion has a key role to play in the energy market of the future. Major projects, like ITER in France and STEP in the UK, are providing potential avenues for solutions of this clean, abundant, and safe energy. Robotics and remotely operated tools are a fundamental part of operating fusion power plants, therefore, a fusion power plant must be designed to be inspected, maintained, and upgraded remotely using robots.
This exciting project will focus on different qualitative and quantitative risk assessment techniques, considering their suitability for risk analysis of the robotics-based maintenance and/or decommissioning of a nuclear fusion plant. System failure modes and contributing elements in success of a mission will be identified and analysed, followed by modelling a variety of threats and their effects on the mission, as well as process recovery and restoration under uncertainty.
Entry Requirements: Starting October 2027, we require an enthusiastic graduate with a 1st class degree in engineering, maths or a relevant discipline, preferably at Masters level (in exceptional circumstances a 2:1 degree can be considered).
This position is open to UK/EU/overseas students. Funding sources can be found at https://www.nottingham.ac.uk/pgstudy/funding/postgraduate-funding.aspx
This position is open until filled. Early application is strongly encouraged.
Aim: To develop a modelling approach for nuclear fusion plant maintenance and decommissioning processes.
More than 80% of the world's energy still comes from fossil fuels. Climate change and diminishing fuel reserves mean that it is becoming essential to find alternative, sustainable technologies to supply a growing global population. With no greenhouse gas emissions, inherent safety features and virtually limitless fuels, nuclear fusion has a key role to play in the energy market of the future. Major projects, like ITER in France and STEP in the UK, are providing potential avenues for solutions of this clean, abundant, and safe energy. Robotics and remotely operated tools are a fundamental part of operating fusion power plants, therefore, a fusion power plant must be designed to be inspected, maintained, and upgraded remotely using robots.
This exciting project will focus on different qualitative and quantitative risk assessment techniques, considering their suitability for risk analysis of the robotics-based maintenance and/or decommissioning of a nuclear fusion plant. System failure modes and contributing elements in success of a mission will be identified and analysed, followed by modelling a variety of threats and their effects on the mission, as well as process recovery and restoration under uncertainty.
Entry Requirements: Starting October 2027, we require an enthusiastic graduate with a 1st class degree in engineering, maths or a relevant discipline, preferably at Masters level (in exceptional circumstances a 2:1 degree can be considered).
This position is open to UK/EU/overseas students. Funding sources can be found at https://www.nottingham.ac.uk/pgstudy/funding/postgraduate-funding.aspx
This position is open until filled. Early application is strongly encouraged.
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Job details
- Reference
- ENG417
- Category
- PhD / Doctoral
- Subject
- Engineering
- Start date
- 1 Oct 2027
- Posted
- 28 Oct 2026
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