About the Project
This project develops learning-driven and optimisation-based methods to keep cities moving under disruption, with a core focus on chance-constrained optimisation for risk-aware decision making. You will build calibrated digital twins of urban corridors and highways and co-design prediction, control, and contingency policies for signals, routing, depots, and EV charging. Methods may include graph neural networks for network-state estimation, multiagent reinforcement learning for cooperative control, Bayesian and causal tools for uncertainty handling, evolutionary and quality-diversity search to generate robust policy portfolios, and risk-sensitive formulations (violation probabilities, quantile/CVaR objectives, distributionally robust models) to guarantee service levels under stochastic demand, incidents, and weather.
Name of primary supervisor/CDT lead:
Yue Xie [contact details available in the full listing] https://www.lboro.ac.uk/departments/compsci/staff/yue-xie/
Entry requirements:
Applicants should hold a first-class or upper second-class honours degree (UK 2:1 or equivalent) in Computer Science, Engineering, Mathematics, Transportation/OR, or a closely related field. A Master’s degree or equivalent research experience is preferred.
Strong programming skills in Python and experience in at least one of machine learning, optimisation, traffic or network modelling.
Evidence of research readiness such as a dissertation, publications or preprints, open-source code, or industry R&D.
English language requirements:
Applicants must meet the minimum English language requirements. Further details are available on the International website (http://www.lboro.ac.uk/international/applicants/english/).
Bench fees required: No
Closing date of advert: 1st September 2026
Start date: July 2026, October 2026, February 2027
Full-time/part-time availability: Full-time 3 years
Fee band: 2025/26 Band RA (UK £5,006, International £22,360)
How to apply:
All applications should be made online. Under programme name, select Computer Science. Please quote the advertised reference number: CO/YX-SF2/2026 in your application.
To avoid delays in processing your application, please ensure that you submit a CV and the minimum supporting documents.
The following selection criteria will be used by academic schools to help them make a decision on your application. Please note that this criteria is used for both funded and self-funded projects.
Please note, applications for this project are considered on an ongoing basis once submitted and the project may be withdrawn prior to the application deadline, if a suitable candidate is chosen for the project
Project search terms:
computer science, intelligent traffic systems; multi-agent system; chance-constrained optimisation; evolutionary optimisation; digital twin traffic networks; EV routing and charging; network resilience and incident response
Email Address Sci:
[contact details available in the full listing]
