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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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Job Details
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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