Artificial Intelligence-Guided Surface Coating Design for Fusion Engineering
Not stated
- Funding
- Self-Funded PhD Students Only
- Application deadline
- Year-round applications
About the project
About the Project Fusion power plants demand plasma-facing and structural surfaces capable of surviving thermal loads, particle erosion, hydrogen isotope permeation, and neutron-induced degradation simultaneously — conditions no single legacy coating system was designed for. Surface engineering (refractory-metal coatings, functionally graded interfaces, and multilayer claddings produced by advanced manufacturing routes such as additive manufacturing, cold spray, and vapour deposition) is the primary line of defence for underlying structural materials, yet coating design still relies heavily on iterative, trial-and-error experimental campaigns that cannot keep pace with the accelerating timelines of fusion demonstrator programmes. This project proposes a paradigm shift from empirical to AI-guided coating design: building a generative, physics-informed machine learning framework that learns process–structure–property relationships across existing coating datasets and then proposes, screens, and ranks novel coating architectures and manufacturing parameter sets before any physical trial is needed. This is a desk-based, fully data-driven PhD project, where all work is carried out through data curation, physics-based simulation, and machine learning model development, making it well suited to self-funded or part-time study. Prerequisite : The applicant must have adequate AI/ML skills. How to Apply Interested applicants should check Heriot-Watt University admission requirements at https://www.hw.ac.uk/about/our-schools/engineering-and-physical-sciences/research/postgraduate-research/how-to-apply . If you meet the admission and self-funding requirements, send the following documents to a.zia@hw.ac.uk A CV A short (1-page) statement of research interest Academic transcripts