Hierarchical Failure Analysis and Digital Twin Modelling of Next-Gen Hydrogel Coatings
About the Project
This project is part of cohort 3 of the EPSRC CDT in Developing National Capability for Materials 4.0, with the Henry Royce Institute.
This PhD Project sets the foundation for a new class of smart, durable hydrogel coatings designed using digital-first principles. By fusing materials science, machine learning, and advanced simulation, the project offers an exciting opportunity to redefine how we engineer and deploy functional surfaces for next-generation biomedical and medical implants. The project is supported by university of Leeds, university of Strathclyde and CN Technology Service Ltd.
Background
Hydrogel coatings represent a critical interface technology for biomedical applications, yet their widespread adoption is severely limited by fundamental challenges in adhesion durability and mechanical integrity under real-world cyclic loading conditions. Structural limitations in current laboratory tests mean that hydrogel-coated prototypes cannot always be mechanically tested, complicating long-term performance evaluation for commercial development.
Research objectives
This project proposes developing an integrated digital twin platform that bridges experimental characterisation, multi-physics modelling, and deep learning to enable predictive design and optimization of hydrogel coating systems. The innovation lies in establishing a closed-loop workflow specifically for interfacial mechanics, with multi-scale integration and physics-informed machine learning and experimental design optimisation.
Training and Career Development
You will gain interdisciplinary skills in machine learning, medical image analysis, and coating design. As a PhD student within university of Leeds and co-supervised by staff at University of Strathclyde and CN Technology Service Ltd, there will be opportunities to contribute to wider activities related to precision measurement and transferable skill training. Groups of researchers working on aligned projects or using similar methods meet regularly to share ideas and best practice. We will support student long-term career ambitions through bespoke training and encourage external secondments, laboratory visits or participation at international conferences.
Skills Required
Candidates should hold a good bachelor’s degree (first or upper second-class honours degree) or an MEng/MSc degree in a relevant engineering/science subject.
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