Research Fellow in Machine Learning-Driven Corrosion Modelling in Bio-feedstock Refining
Do you have a strong technical background in Corrosion, Machine Learning and Numerical Modelling? Are you interested in working with industry to develop Machine Learning methodologies and protocols needed to support the uptake of renewable bio-feedstocks as alternatives to petroleum-based feedstocks in the production of fuel?
There are strong economic, environmental, regulatory and geopolitical drivers to replace petroleum-based feedstocks with renewable, bio-based feedstocks in the production of fuel. However, bio-feedstocks have significantly different chemistries than crude oil that may accelerate the corrosion of refinery infrastructure, requiring the development of new knowledge, experimental and theoretical methods to corrosion management. Sponsored by bp and working with an internationally leading team from Imperial College, London (ICL), University College, London (UCL) and the University of Illinois, Urbana-Champaign (UIUC), this project aims to create the fundamental understanding and reliable corrosion prediction tools needed to accelerate the uptake of bio-feedstocks.
This project, based at the University of Leeds, will focus on the development of a range of Machine Learning, AI and optimisation tools and methodologies for bio-feedstock corrosion management, that can accommodate new chemistries and material combinations and predict material performance (corrosion rates, lifespan, operating limits) in refinery operations. This will require frequent interactions with bp and with experimentalists at UIUC, to develop adaptive experimental sampling methods, and with colleagues at ICL and UCL, to implement Physics-informed Machine Learning methods within an overall system modelling software tool.
We are open to discussing flexible working arrangements.
To explore the post further or for any queries you may have, please contact:
Prof Richard Barker, Professor in Corrosion Science and Engineering
Tel: +44 (0)113 343 2206
Email: R.J.Barker @ leeds.ac.uk
Please note that this post may be suitable for sponsorship under the Skilled Worker visa route but first-time applicants might need to qualify for salary concessions. For more information, please visit the Government’s Skilled Worker visa page.
For research and academic posts, we will consider eligibility under the Global Talent visa. For more information, please visit the Government’s page, Apply for the Global Talent visa.
What we offer in return
* 26 days holiday plus approx.16 Bank Holidays/days that the University is closed by custom (including Christmas) - That’s 42 days a year!
* Generous pension scheme options plus life assurance .
* Health and Wellbeing: Discounted staff membership options at The Edge, our state-of-the-art Campus gym, with a pool, sauna, climbing wall, cycle circuit, and sports halls.
* Personal Development: Access to courses run by our Organisational Development & Professional Learning team.
* Access to on-site childcare, shopping discounts and travel schemes are also available.
And much more!
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Location Leeds - Main Campus Faculty/Service Faculty of Engineering & Physical Sciences School/Institute School of Mechanical Engineering Section Institute of Functional Surfaces Category Research Grade Grade 7 Salary £41,064 to £48,822 p.a. Working Time 37.5 hours per week Post Type Full Time Contract Type Fixed Term (Up to 34 months with a potential extension for a further 12 months pending industry approval - to complete specific time limited work) Release Date Friday 24 April 2026 Closing Date Wednesday 27 May 2026 Reference EPSME1206 Downloads Candidate Brief (PDF)
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