UOB
Scalable Intelligent Planning for Partially Observable Uncertain Systems
✓ Fully Funded
🎓 Control Systems
🎓 Machine Learning
🎓 Stochastic Processes
🎓 Systems Engineering
machine learning
optimisation
infrastructure systems
planning
stochastic modelling
uncertainty
PhD project developing scalable planning and decision-making methods for complex uncertain infrastructure systems.
Project Description
This PhD project focuses on developing scalable intelligent planning methods for managing large-scale infrastructure systems under uncertainty.
The research will address challenges in systems such as power grids, water networks, and transportation systems, where only partial and uncertain data is available. It will explore modelling and optimisation techniques for nonlinear and stochastic systems, aiming to support adaptive decision-making for maintenance and operations.
The project will develop methodologies to prioritise actions, balance resource allocation, and minimise cascading failures in complex interconnected systems.
Entry Requirements
Strong academic background in Mathematics, Engineering, or Computer Science
• Knowledge of modelling, optimisation, or stochastic systems
• Interest in large-scale systems and infrastructure management
• Strong analytical and problem-solving skills
• Knowledge of modelling, optimisation, or stochastic systems
• Interest in large-scale systems and infrastructure management
• Strong analytical and problem-solving skills
How to Apply
Register interest and follow University of Bristol application process.
Eligibility
UK/Home
EU
International
Supervisor Profile
DK
Dr Kaiqiang Zhang
University of Bristol, School of Engineering Mathematics and Technology
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