Human-Centred Stability, Control and Decision-Making for Intelligent Transportation Systems

About the project

About the Project Are you passionate about intelligent systems, autonomous technologies, and the future of transportation? This PhD opportunity offers the chance to contribute to the development of next-generation control and decision-making technologies that will enhance the safety, efficiency, resilience, and performance of future transportation systems. Modern transportation is undergoing a profound transformation driven by electrification, automation, connectivity, and artificial intelligence. From road vehicles and rail systems to emerging mobility platforms and autonomous transport technologies, there is a growing need for intelligent control systems capable of operating safely and effectively in complex and uncertain environments. A key challenge across many transportation domains is maintaining stability, safety, and optimal performance while responding to changing operating conditions, environmental disturbances, and evolving system demands. Advances in sensing, computation, machine learning, and distributed actuation are creating exciting opportunities to develop more adaptive, intelligent, and responsive control strategies capable of supporting future mobility systems. This research will investigate advanced approaches to stability, control, and autonomous decision-making within intelligent transportation systems. The project will explore how modern control theory, artificial intelligence, machine learning, optimisation, and data-driven modelling techniques can be integrated to improve system performance, robustness, efficiency, and operational safety. Depending on the candidate's interests and project direction, applications may include intelligent road vehicles, electric and autonomous transportation systems, rail transport, connected mobility platforms, smart infrastructure, robotic transportation technologies, and other emerging transport solutions. The research will involve the development of mathematical models, simulation environments, and intelligent control frameworks to analyse and optimise the behaviour of complex transportation systems under realistic operating conditions. As a PhD candidate, you will: Develop expertise in intelligent control systems, autonomous decision-making, and transportation technologies. Investigate advanced modelling, optimisation, and machine learning approaches for dynamic systems. Explore novel methods for improving stability, safety, efficiency, and resilience in transportation applications. Design and evaluate intelligent algorithms using state-of-the-art simulation and computational tools. Work at the intersection of artificial intelligence, control engineering, transportation systems, and automation. Contribute to research that supports the future of sustainable, connected, and autonomous mobility. This interdisciplinary project combines elements of control engineering, artificial intelligence, robotics, transportation systems, and digital technologies, providing an excellent opportunity to work on challenges that are shaping the future of mobility worldwide. We are seeking highly motivated candidates with strong analytical and problem-solving skills and a background in engineering, computer science, mathematics, physics, or related disciplines. Experience in areas such as control systems, machine learning, optimisation, robotics, simulation, programming, data science, or intelligent systems would be beneficial but is not essential. Join us in developing the intelligent technologies that will power the next generation of transportation systems and help shape the future of safe, efficient, and sustainable mobility. The School of Mechanical, Electrical and Manufacturing Engineering has seen 100% of its research impact rated as 'world-leading' or 'internationally excellent' (REF, 2029). Supervisors · Primary supervisor: Dr Behnaz Sohani Full-time: 3.5 years, 4 years Part-time: 7 years, 8 years Start date: October 2026, January 2027, April 2027, July 2027, October 2027. Entry requirements 2:1 honour degree (or equivalent) English language requirements Applicants must meet the minimum English language requirements. Further details are available on the International website. How to apply Applications should be made online . Under programme name, select ‘Mechanical and Manufacturing Engineering/Electronic, Electrical & Systems Engineering’ and mention the Primary supervisor: Dr Behnaz Sohani in your application and proposal. To avoid delays in processing your application, please ensure that you submit your 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. Funding Notes Fees for 2024-25 (per academic year) UK: £5,238 International: £29,500

Research areas