PhD Studentship: Materials Science and Engineering: Accelerating Sustainable Alloy Development using Machine Learning

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.

1. Optimizing nuclear-grade low alloy steel for reactor pressure vessels.
2. Designing sustainable, corrosion-resistant alloys for automotive brake cladding to meet Euro 7 standards.
3. 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.