Program Overview
Research profile
The Institute's research aims to improve the performance of materials and structures, including their design, manufacturing, integrity and use, through a combined theoretical approach. Its vision is to become the leading recognised international provider of materials and manufacturing research, addressing current major societal questions.
Find out about the exciting research we do in this area. Browse profiles of our experts, discover the research groups and their inspirational research activities you too could be part of. We’ve also made available extensive reading materials published by our academics and PhD students.
Learn more about research in this area.
Browse the work of subject-relevant research groups
Advanced Powertrain and Fuels
Biomedical Engineering
Assessment of Structures and Materials under Extreme Conditions
Bioprocess and Biopharmaceutical Engineering
Brunel Innovation Centre
Digital Manufacturing
Brunel Composites Centre
Design and Manufacturing
Brunel Centre for Advanced Solidification Technology (BCAST)
Flood, Coastal and Water Engineering
Two Phase Flow and Heat Transfer
Resilient Structures and Construction Materials
Non-traditional Manufacturing Technologies
Experimental Techniques Centre
Energy Efficient and Sustainable Technologies
Heat Pipe and Thermal Management
Organ-on-a-Chip
Institute of Energy Futures
Quality Engineering and Smart Technology
Institute of Materials and Manufacturing
Geotechnical and Environmental Engineering
Sustainable Energy Use in Food Chains
Resource Efficient Future Cities
Institute of Digital Futures
Flood Risk and Resilience
Wolfson Centre for Sustainable Materials Development and Processing
Mechanics of Solids and Structures
Robotics and Automation
Equitable Development and Resilience
You can explore our campus and facilities for yourself by taking our virtual tour.
Program Outline
Research journey
This course can be studied 3 years full-time or 6 years part-time, starting in January. Or this course can be studied 3 years full-time or 6 years part-time, starting in October. Or this course can be studied 3 years full-time or 6 years part-time, starting in April.
Find out about what progress might look like at each stage of study here: Research degree progress structure.
Careers and your future
You will receive tailored careers support during your PhD and for up to three years after you complete your research at Brunel. We encourage you to actively engage in career planning and managing your personal development right from the start of your research, even (or perhaps especially) if you don't yet have a career path in mind. Our careers provision includes online information and advice, one-to-one consultations and a range of events and workshops. The Professional Development Centre runs a varied programme of careers events throughout the academic year. These include industry insight sessions, recruitment fairs, employer pop-ups and skills workshops.
In addition, where available, you may be able to undertake some paid work as we recognise that teaching and learning support duties represent an important professional and career development opportunity.
Find out more.
Find a supervisor
Our researchers create knowledge and advance understanding, and equip versatile doctoral researchers with the confidence to apply what they have learnt for the benefit of society. Find out more about working with the Supervisory Team.
You are welcome to approach your potential supervisor directly to discuss your research interests. Search for expert supervisors for your chosen field of research.
PhD topics
While we welcome applications from student with a clear direction for their research, we are providing you with some ideas for your chosen field of research:
A sustainability analysis of sea ports, supervised by
Colin Axon
A Systems Approach to Promoting Sustainable Bioeconomy through International Development of Novel Biorefinery System Concepts, supervised by
Kok Siew Ng
Additive manufacturing and sustainability, supervised by
Eujin Pei
Analysis of the effect of Natural Flood Management measures in water levels, supervised by
Pedro Martin-Moreta
Analytical and numerical modeling of innovative strengthening materials (Fiber Reinforced Polymer and Textile Reinforced Mortar) applied to brittle supports, supervised by
Elisa Bertolesi
Antimicrobial resistance in marine mammals (seals) from polluted waters, supervised by
Gera Troisi and Ashley Houlden
Automatic computational fluid-dynamics, supervised by
James Tyacke
Autonomous robots for non-disruptive inspection of utility and sewage systems, supervised by
Md Nazmul Huda
Can AI based robot car win the race, supervised by
Dong Zhang
CFD modelling of plasma flow control, supervised by
James Tyacke
Climate resilience of interdependent transport and energy infrastructure informed by emerging digital technologies, supervised by
Sotirios Argyroudis
Crystal Plasticity Modelling of Hexagonal Closed-Pack (HCP) Materials for Manufacturing, supervised by
Rui Ramos Cardoso
Deep learning methods in multi-omics data analysis for critical disease, supervised by
Yang Yang
Design, development, and optimisation of a six-legged robot for hybrid walking and manipulation in challenging environments, supervised by
Mingfeng Wang
Developing a device for marine life and water quality monitoring, supervised by
Gera Troisi
Developing Sustainable Decarbonised Polygeneration System Concept for the Production of Hydrogen, Chemicals and Energy, supervised by
Kok Siew Ng
Developing Sustainable Waste Management Strategies through Innovative Resource Recovery and Valorisation Technologies, supervised by
Kok Siew Ng
Development of next generation bioreactor models, supervised by
Dale McClure
Development of resilient hospitals through enhanced built environment design and research, supervised by
Kangkang Tang
Digital Stone: Robotic Construction of a Masonry Arch Bridge, supervised by
Michael Rustell and Tatiana Kalganova
Digital twin in continuous UF/DF step of bioprocess, supervised by
Yang Yang
Dynamics of seawater intrusion in heterogeneous coastal aquifers, supervised by
Ashraf Ahmed Mohamed
Fracture assessment of large-scale structural components, supervised by
Marius Gintalas
Intelligent, Interpretable and Adaptive Design of Steel Structures using Deep Learning and NLP, supervised by
Michael Rustell and Tatiana Kalganova
Large Language Models (LLM) for Automated Finite Element Analysis, supervised by
Michael Rustell and Tatiana Kalganova
Life cycle assessment and circular economy for built environment, supervised by
Muhammad Shafique
Low-carbon cementitious composites from brick waste powder, supervised by
Seyed Ghaffar
Machine learning for decision making: how to choose the optimal strategy for stratified medicine life cycle, supervised by
Yang Yang
Machine learning for sustainable transportation systems, supervised by
Muhammad Shafique
Next generation aeroacoustically and aerodynamically efficient aerofoil, supervised by
Tze Pei Chong
Next generation electric vehicles, supervised by
Dong Zhang
Optimisation of geothermal energy extraction, supervised by
James Tyacke
Prediction of early-age cracking in structural concrete, supervised by
Kangkang Tang
Reliability Analysis of Adhesively Bonded Fibre Reinforced Polymer Composites, supervised by
Sadik Omairey and Mihalis Kazilas
Study of stray current induced corrosion in railway construction, supervised by
Kangkang Tang
Sustainable production of high-value compounds using cyanobacteria, supervised by
Dale McClure
Sustainable production of Vitamin K1, supervised by
Dale McClure
Sustainable products & processes - help industry ditch the plastic and toxic chemicals!, supervised by
Gera Troisi
Swarm of multiple co-operative and autonomous low-cost robots for search and rescue, supervised by
Md Nazmul Huda
The sustainability of hydrogen production for future energy uses, supervised by
Colin Axon and Peter Hewitson
Toward automated vehicle control beyond the stability limits via active drifting control, supervised by
Dong Zhang
Use of Large Language Models (LLM) as a Structural Engineering Design Assistant, supervised by
Michael Rustell and Tatiana Kalganova
Using Machine Learning to Simulate Macroscopic phenomena for Fluid Dynamics, supervised by
Nadine Aburumman
