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Research Fellow in Machine Learning in Carbon Capture Utilisation & Storage

Do you have a strong technical background in Machine Learning and Numerical Modelling? Are you interested in working with industry to develop Machine Learning methodologies and protocols needed to deliver resilient, interoperable and safe CO2 transport infrastructure in Europe?

Carbon Capture Utilisation and Storage (CCUS) is a key element in the European strategy for carbon neutrality by 2050. The University of Leeds is part of a large consortium of 24 partners from 7 European countries, consisting of leading international universities, research organisations and leading international energy companies, including bp, EDF, Equinor and Shell, working to ensure a sustainable CCUS industry at scale. The overall goal is to ensure that the transport infrastructure is capable of handling CO2 streams at different flow rates, pressures and states and with different compositions and impurities without posing unacceptable risks for the infrastructure, the environment and populations. 

The aim of this project is to develop numerical models and Machine Learning and AI methodologies, including Physics Informed Neural Networks (PINNs) and Symbolic Regression tools, to predict chemical reactions, impurity evolution along pipelines and associated corrosion threats in dense phase CO2 streams with impurities. Working with regulators, standardisation and certification bodies, technology developers and industry, the models will be used to determine optimal pipeline operating conditions and develop guidelines for pipeline operation, providing practical recommendations for impurity concentrations ensuring safe and efficient transport of dense phase CO2. 

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 36 months - Starting from 01 June 2026 and to end by 31 May 2029 - to complete specific time limited work)
Release Date
Tuesday 14 April 2026
Closing Date
Tuesday 28 April 2026
Reference
EPSME1205
Downloads
Candidate Brief (PDF)
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