Publisher
source

University of Bristol

Top university

PhD in Shaping Collective Traffic Behaviour through Multi-Agent Learning-Based Control University of Bristol in United Kingdom

Degree Level

PhD

Field of study

Computer Science

Funding

Competition funded PhD project for students worldwide. The post indicates a funded PhD project, but no stipend amount, tuition details, or other financial breakdown is provided.

Deadline

Dec 31, 2026

Country flag

Country

United Kingdom

University

University of Bristol

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Keywords

Computer Science
Machine Learning
Mechanical Engineering
Electrical Engineering
Mathematics
Intelligent Transportation Systems
Industrial Engineering
Reinforcement Learning
Robotics
Traffic Flow Theory
Connected Vehicle
Control System
Autonomous Vehicle

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About this position

PhD opportunity at the University of Bristol in Shaping Collective Traffic Behaviour through Multi-Agent Learning-Based Control.

This project sits at the intersection of artificial intelligence, machine learning, control systems, autonomous driving, and intelligent transportation systems. The research focuses on mixed autonomy, where connected autonomous vehicles (CAVs) and human drivers share the road, and asks how learning-based control can shape collective traffic behaviour toward efficiency, safety, and resilience.

You will explore topics such as deep reinforcement learning, multi-agent systems, embodied perception and decision-making, coordination of autonomous vehicles, and the control of mixed human–machine traffic. The project also emphasizes the mathematical theory behind scalable control and coordination methods, with validation in autonomous-driving and traffic simulators and, where useful, real-world mobility data.

The project is hosted by the School of Engineering Mathematics and Technology at the University of Bristol. The post is advertised as a competition funded PhD project for students worldwide.

Eligibility highlights: a strong background in AI, machine learning, mathematics, control, engineering, or a related field; solid programming and mathematical skills; and an interest in learning, control, and autonomous systems. Experience with reinforcement learning or optimisation is helpful but not essential.

Application / enquiry: the page invites you to register your interest via FindAPhD. Informal enquiries are welcome to Dr Kai-Fung Chu at [email protected], with a CV and short statement of interest.

Deadline: Thursday, December 31, 2026.

Funding details

Competition funded PhD project for students worldwide. The post indicates a funded PhD project, but no stipend amount, tuition details, or other financial breakdown is provided.

What's required

Applicants should have a strong background in AI, machine learning, mathematics, control, engineering, or a related field. Solid programming and mathematical skills are expected, along with an interest in learning, control, and autonomous systems acting in the physical world. Experience with reinforcement learning or optimisation is beneficial but not required.

How to apply

Register your interest through the FindAPhD page. For informal enquiries, email Dr Kai-Fung Chu with a CV and short statement of interest.

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