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This project focuses on developing a novel, hybrid Integrated Computational Materials Engineering (ICME) framework to accelerate the discovery of advanced steel powders. By integrating Machine Learning (ML) with physics-based modelling (CALPHAD) and Rapid Alloy Prototyping, the successful candidate will create a “digital-first” workflow to optimize steel alloys for the nuclear and automotive sectors.
Key applications include:
- Optimizing nuclear-grade low alloy steel for reactor pressure vessels.
- Designing sustainable, corrosion-resistant alloys for automotive brake cladding to meet Euro 7 standards.
- Developing Fe-based alloys for plasma-clad components as eco-friendly alternatives to Cobalt-Chromium.
This research is part of the IGNITE project, aiming to transition the UK steel industry toward a circular, net-zero economy.
Funding
Covers full tuition, £21,805 stipend (2026/27), plus up to £1,000 yearly for research costs.
