At a Glance
- Tasks: Develop machine learning tools to predict and manage corrosion in bio refineries.
- Company: Join a collaborative research team from top universities and bp.
- Benefits: Receive a tax-free maintenance grant of £21,805 per year for 3.5 years.
- Other info: Engage with leading researchers and gain unique insights into industrial corrosion challenges.
- Why this job: Contribute to sustainable technology and tackle real-world challenges in renewable fuels.
- Qualifications: First class or upper second class degree in Mechanical Engineering, Computer Science, or Data Analytics.
The predicted salary is between 21805 - 21805 £ per year.
Faculty of Engineering and Physical Sciences EPSRC Project Proposals 2026/27
Project Title: Machine Learning Driven Corrosion Modelling in Bio Feedback Refining
School/Faculty: Mechanical Engineering
Closing Date: 26 June 2026
Eligibility: UK Only
Funding: School of Mechanical Engineering Scholarship, in support of the IMPACT-Bio Research Grant, providing full academic fees, together with a tax-free maintenance grant at the standard UKRI rate of £21,805 per year for 3.5 years.
Lead Supervisor: Professor Richard Barker
Co-supervisors: Professor Harvey Thompson, Dr Joshua Owen
Project summary: The global shift toward renewable bio-based fuels is creating exciting new scientific and engineering challenges. Compared with traditional crude oil, bio feedstocks behave very differently during processing, sometimes causing much faster corrosion of refinery equipment. Understanding these behaviours is essential if society is to move confidently toward low carbon fuels. This PhD studentship offers the opportunity to contribute directly to this challenge by developing modern data-driven tools that help predict and manage corrosion in next generation bio refineries.
The project brings together leading researchers from University of Leeds, Imperial College London, University College London, and the University of Illinois at Urbana–Champaign, supported by industrial scientists at bp. Regular engagement with bp and the experimental team in Illinois will provide unique insight into industrial corrosion challenges and support the development of adaptive, data-driven sampling strategies to accelerate experimental progress. This environment will provide you with a unique perspective on how data, modelling, experiments, and industrial needs come together in an emerging area of sustainable technology. The research will also contribute to the creation of high throughput approaches for assessing the corrosivity of bio refinery environments.
Entry requirements: A first class or an upper second class British Bachelors Honours degree (or equivalent) in an appropriate discipline.
Subject Area: Mechanical Engineering, Computer Science & IT, Data Analytics
Keywords: Optimisation, adaptive sampling, corrosion, corrosion sampling, data analysis, machine learning
Salary: £21,805 per annum
PhD Studentship: Machine Learning Driven Corrosion Modelling in Bio Feedback Refining in Leeds employer: University of Leeds
The Faculty of Engineering and Physical Sciences at the University of Leeds offers an exceptional environment for aspiring researchers, particularly in the innovative field of Machine Learning Driven Corrosion Modelling. With a strong focus on collaboration with leading institutions and industry partners like bp, this PhD studentship not only provides generous funding but also fosters a vibrant work culture that encourages intellectual growth and hands-on experience in tackling real-world challenges in sustainable technology.
