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.

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