Our studentships

Supervisors: Dr Aakash Bansal, Dr Yasir Ali, Dr Claire Guo and Professor Will Whittow
Schools: Civil Engineering (ABCE) and Mechanical, Electrical and Manufacturing Engineering (MEME)

Drivers, including autonomous vehicles, fail to make the right decisions often due to misjudgement, faults of other road users or information not perceivable by sensors.

One of the biggest reasons for failure is perception-based sensors that cannot see beyond obstacles and can miss the right information because of exclusivity.

This PhD will develop new RF-based sensors that can see and collect information beyond obstacles - helping drivers and autonomous vehicles make the right decisions in advance, enabling a more active inclusive mobility.

Supervisors: Professor Andrew Morris, Dr Jingjing Jiang, Dr Jo Barnes and Dr Cecilia Landa-Avila
Schools: Design and Aeronautical and Automotive Engineering

Our main question here is how equitable might a connected transport system be?

Addressing this challenge involves considering transport users who are either physically or cognitively disabled or impaired to explore how they may be disadvantaged by a futuristic transport system where information about transport and travel may only be digitally available.

There are several user-groups who may not cope well with this eventuality, including autistic travellers, individuals with dyslexia, partially sighted travellers, and individuals with ADHD. Older people may also be disadvantaged by digital systems perhaps because of dementia or other impacts of ageing on cognitive processes.

You will examine different transport scenarios, engage with potentially disadvantaged users and use a Design Thinking approach to understand their needs through empathy and focus groups. You will develop unique co-designed solutions to those who may be most disadvantaged.

Supervisors: Dr Haitao He, Dr Marco Discacciati, Dr Miguel Martinez and Dr Eve Zhang
Schools: Civil Engineering (ABCE), Mathematical Sciences, and Aeronautical and Automotive Engineering

As cities grow, ensuring transport systems are equitable, accessible and affordable for all becomes increasingly critical and complex. You will advance simulation-based optimisation methods to address these challenges.

Leverage large-scale data sets, cutting-edge simulation tools and advanced optimisation techniques, you will model and evaluate the effectiveness of various mobility scenarios. Your findings will provide actionable insights to shape policies and technological interventions that promote inclusive transport systems.

You will also have opportunities to collaborate with leading researchers, policymakers and industry partners as well as the chance to make a tangible impact on the future of urban mobility.

Supervisors: Dr Brendan Lawson, Dr Asya Natapov and Professor Diwei Zhou
Advisor: Professor Richard Ferguson
Schools: Social Sciences and Humanities, Civil Engineering (ABCE), Mathematical Sciences, and Sport, Exercise and Health Sciences

You will investigate the impact of different intervention types - including structural measures, modified materials, vegetation, drainage and covers or barriers - on the hydromechanical behaviour and properties of earthworks, focusing on their vulnerability to climate and weather extremes such as floods, droughts and cyclical wet-dry conditions.

You will explore whether novel interventions can be developed to reduce vulnerability and enhance recovery from these extremes, evaluating which interventions provide the most significant improvements to resilience.

In addition, your research will assess intervention carbon- and cost-effectiveness and develop strategies to optimise the type and timing of their deployment.

Supervisors: Dr Thomas Steffen, Professor Mahroo Eftekhari, Dr Zhengyu Lin and Dr Lu Tian
Schools: Aeronautical and Automotive Engineering, Civil Engineering (ABCE) and Wolfson

Public transport in England has a notoriously poor utilisation rate outside of big cities which means it is neither effective nor sustainable.

You will explore ways to reinvent public transport by using AI to predict demand and to adapt to individual requests. The idea is to understand the demand on a granular level, to schedule appropriate vehicles and create virtual bus stops that are more convenient and accessible for passengers.

You will also consider inclusivity questions such as rural settings, gender, age and demographics categories - simulating the effect of intelligent dynamic scheduling on individual passengers, looking especially for ways to mitigate any existing discrimination.