Research Fellow in Machine Learning-Driven Corrosion Modelling in Bio-feedstock Refining
University of Leeds, Leeds
Research Fellow in Machine Learning-Driven Corrosion Modelling in Bio-feedstock Refining
£41064-£48822
University of Leeds, Leeds
- Full time
- Temporary
- Onsite working
Posted today, 25 Apr | Get your application in now to be one of the first to apply.
Closing date: Closing date not specified
Job ref: 32f1e1fd1816441a9557ce2541f2eace
Location ref: Leeds
Full Job Description
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.
o 26 days holiday plus approx.16 Bank Holidays/days that the University is closed by custom (including Christmas) - That's 42 days a year!
o Generous pension scheme options plus life assurance.
o 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.
o Personal Development: Access to courses run by our Organisational Development & Professional Learning team.
o Access to on-site childcare, shopping discounts and travel schemes are also available.
And much more!
Direct job link
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