Our studentships
Our transdisciplinary PhD projects explore a variety of exciting, cutting-edge topics.
We are pleased to offer seven fully funded PhD studentships.
Each interdisciplinary project will contribute to our growing knowledge and expertise in sustainable, intelligent and climate change-resilient engineered slopes.
Our studentship topics
The application portal will open soon. Meanwhile, please review the opportunities on offer.
Earthwork interventions and rehabilitation strategies for enhanced resilience to extreme climate events
Supervisors: Dr Haitao Lan, Dr Ana Blanco, Dr Mark Jepson and Dr Alister Smith
Schools: Civil Engineering (ABCE) and Materials (AACME)
This PhD project 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.
Automating earthworks construction and maintenance processes
Supervisors: Dr Ana Blanco, Dr Laura Justham, Dr Matthew Frost and Dr Alister Smith
Schools: Civil Engineering (ABCE) and Wolfson
This PhD project will explore the potential role of automation in the construction and maintenance of earthworks, with the goal of developing a system-of-systems framework for the real-time control and delivery of ‘printed’ geotechnical structures.
The purpose is to remove workforces from hazardous environments and to enhance the sustainability, speed and quality of construction and maintenance processes.
Key considerations will include tracking soil from excavation through treatment, transport, placement and testing to produce digital records, optimising soil engineering properties in real-time and integrating soil construction with structural elements such as soil nails, retaining structures and drains.
Slope drainage systems: Hydromechanical behaviour, design, performance and climate resilience
Supervisors: Dr Alister Smith, Dr Matthew Frost and Dr Haitao Lan
Schools: Civil Engineering (ABCE)
This PhD is co-funded and co-supervised by Network Rail.
The project's aims are to enhance understanding of how drainage systems impact slope hydromechanical behaviour and then develop an improved design and maintenance framework for long-term performance and climate resilience.
You will assess the influence of various slope drainage systems on pore pressures, groundwater movement, shear key effects and hydrological interception.
In addition, you will explore innovative materials to enhance drainage system performance, evaluate historical asset records, and integrate physical and numerical modelling.
Advancing InSAR for predictive railway earthworks monitoring and risk management
Supervisors: Dr Alister Smith and Professor Craig Hancock
Schools: Civil Engineering (ABCE)
This PhD is co-funded and co-supervised by Network Rail.
The aim of this project is to enhance the utility of InSAR (Interferometric Synthetic Aperture Radar) in railway earthworks asset management.
You will deliver improved InSAR techniques - including an advanced SBAS methodology and AI-driven spatial resolution upgrades - for more accurate monitoring of railway earthworks.
Additionally, you will integrate environmental data through data fusion and develop automated machine learning tools for anomaly detection and risk assessment. The effectiveness of the developed approach will be validated and refined through comparisons with alternative observations and measurements.
Soil micro-structure evolution during deterioration driven by environmental cycles
Supervisors: Dr Mark Jepson, Dr Matthew Frost and Dr Alister Smith
Schools: Civil Engineering (ABCE) and Materials (AACME)
This PhD project aims to advance understanding of how environmental (weather) cycles drive deterioration in geomaterials.
You will focus on the soil micro-structure evolution and how this links to macro-scale behaviour, using advanced laboratory tests, imaging techniques and computer simulations.
The outcomes will support the development of strategies to mitigate these deterioration processes and improve the resilience of geotechnical infrastructure.
We are delighted to have successfully recruited to the opportunities described below. Please feel free to review the projects our students are engaged in.
Advanced sensing and AI-driven diagnostics for earthwork condition assessment and deterioration detection
Supervisors: Dr Craig Hancock, Dr Hui Fang, Professor James Flint and Dr Alister Smith
Schools: Civil Engineering (ABCE), Computer Science and Wolfson
Current earthworks assessment remains largely based on visual inspection of the surface. Subsurface deterioration is often missed, and failures occur without warning.
This PhD project aims to develop novel diagnostic techniques for earthwork asset condition appraisal and deterioration detection, helping us to answer the question: How close to failure is the asset?
By integrating a suite of state-of-the-art sensors and monitoring technologies with data fusion and AI analytics, your research will enable timely identification of deterioration processes and assessment of their evolution/extent.
Probabilistic forecasting of climate change impacts on earthwork deterioration and failure
Supervisors: Dr Ashraf El-Hamalawi, Dr Asma Adnane, Dr Haitao Lan and Dr Alister Smith
Schools: Civil Engineering (ABCE) and Computer Science
This PhD aims to develop capabilities to forecast deterioration and failure in earthworks driven by weather cycles and climate change scenarios to enable a transition from responsive maintenance interventions / renewals to predictive, proactive and targeted approaches that help to avoid failures.
By integrating numerical simulations, probabilistic techniques and AI analytics, earthwork prognoses could be expressed in the form of time-to-failure and / or probability of survival under extreme weather events.