UOL
Machine-learning based optimisation of corrosion inhibitor formulations for CO2-containing aqueous environments
✓ Funded (Competition)
⏰ Closing Soon
🎓 Artificial Intelligence
🎓 Data Science
🎓 Dynamics
🎓 Energy Technologies
🎓 Engineering
🎓 Fluid Mechanics
🎓 Machine Learning
AI-driven materials design
CO2 corrosion
chemical formulation
corrosion modelling
energy infrastructure
fluidic testing systems
high-throughput screening
inhibitor optimisation
surface adsorption
Funded CDT PhD at University of Leeds using machine learning to optimise corrosion inhibitor formulations for CO₂-rich environments. Combines AI, fluidic high-throughput testing, and corrosion science to improve energy infrastructure materials.
Project Description
This PhD at University of Leeds is part of the EPSRC Centre for Doctoral Training in Future Fluid Dynamics in collaboration with SLB.
The project addresses internal corrosion in carbon steel infrastructure, a major issue in energy systems. It focuses on improving corrosion inhibitor formulations used to protect pipelines and equipment exposed to CO₂-containing aqueous environments.
Key research components include:
Development of machine-learning models for corrosion inhibitor optimisation
Design of high-throughput fluidic screening systems
Data-driven analysis of corrosion performance
Chemical formulation optimisation for efficiency and sustainability
Integration of AI with experimental corrosion testing
The goal is to accelerate discovery of environmentally friendly, high-performance corrosion inhibitors and reduce reliance on slow, manual testing methods.
Entry Requirements
Applicants should have:
First-class or strong upper second-class degree (or equivalent)
Background in Engineering, Fluid Mechanics, Chemistry, Data Science, or related fields
Desirable:
Machine learning or AI experience
Interest in materials science or corrosion processes
Programming skills (Python, MATLAB, etc.)
Experience with experimental or data-driven modelling
First-class or strong upper second-class degree (or equivalent)
Background in Engineering, Fluid Mechanics, Chemistry, Data Science, or related fields
Desirable:
Machine learning or AI experience
Interest in materials science or corrosion processes
Programming skills (Python, MATLAB, etc.)
Experience with experimental or data-driven modelling
How to Apply
Apply via University of Leeds CDT application portal:
Steps:
Select Research Postgraduate
Choose EPSRC CDT Future Fluid Dynamics
Upload CV, transcripts, CDT personal statement
No research proposal required
Contact:
Prof Richard Barker – R.J.Barker@leeds.ac.uk
CDT: fluid-dynamics@leeds.ac.uk
Steps:
Select Research Postgraduate
Choose EPSRC CDT Future Fluid Dynamics
Upload CV, transcripts, CDT personal statement
No research proposal required
Contact:
Prof Richard Barker – R.J.Barker@leeds.ac.uk
CDT: fluid-dynamics@leeds.ac.uk
Eligibility
UK/Home
EU
International
Supervisor Profile
PR
Prof Richard Barker
University of Leeds, EPSRC Centre for Doctoral Training in Future Fluid Dynamics
Related Opportunities
AUB
UNI
ULR
UOK