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DC10 Development of deep learning-based surrogate models for predicting failure in meta-material composites

Qualification: Doctor of Philosophy in Engineering (PhD)

Start date: 05 October 2026

Deadline: 31 July 2026

A European Doctoral network on Advanced Meta-materials and Meta-Structures for Adaptable, Resilient and Sustainable renewable energy power plants

Funded by the Marie Skłodowska-Curie Actions (Horizon Europe, Grant Agreement No. 101227175), Met2Adapt aims to recruit 16 PhD candidates who will be employed by one of the 10 partner institutions across Europe.

This project offers the opportunity to tackle one of the most exciting challenges in modern engineering, i.e. understanding and predicting how advanced composite materials become damaged. 1. Develop a high-fidelity simulation framework to model the initiation and growth of damage in complex meta-material composites under realistic loading conditions. 2. Build a scientific machine learning surrogate capable of reproducing and predicting unseen damage and fracture scenarios within seconds once trained and 3. Demonstrate how these intelligent models can be used for real-time structural monitoring and reliability assessment in applications such as wind turbine components.

We are looking for motivated and curious students to join a cutting-edge PhD project at the intersection of fracture mechanics, numerical methods, computational modelling, and scientific machine learning with a background on engineering, mathematics, or physical sciences and eager to apply their skills to solve real-world problems.

Host: University of ÌÇÐÄTV, UK

Awarding PhD entity: University of ÌÇÐÄTV, UK

Applications start: 01/01/2026

Application deadline: 31/07/2026

Earliest project start date: 5/10/2026

How to apply and Informal inquiries: Contact Prof. Emmanouil Kakouris at Emmanouil.Kakouris@warwick.ac.uk,Link opens in a new window attaching also a CV.


More about this project and partners:

European Commission project:

Thu 12 Feb 2026, 11:45 | Tags: Predictive Modelling

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