Research fellow machine learning storage

Working time: 37.5 hours per week Contract type: Fixed term (up to 36 months - Starting from 1st June 2026 and to end by 31st May 2029 - to complete specific time limited work) 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: [email protected] 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.