Cracks and Code: From High-Fidelity Simulations to Fast Scientific Machine Learning Models
This is a fully-funded 4-year PhD position based in the HetSys Centre for Doctoral Training at the University of Warwick.
Project outline
When materials are pushed to their limits, such as during high-speed impacts or other extreme loading events, they can fail in ways that are still not fully understood.
This project offers the chance to uncover the science behind these processes and help design the next generation of safer, stronger materials. You will combine advanced modelling techniques with scientific machine learning to create intelligent simulation tools that accelerate and enhance conventional methods, enabling faster and more accurate predictions of how metals behave under extreme conditions.
Your work will directly address real engineering challenges and advance materials innovation and safety.
Please note that due to the nature of AWE-NST's work, nationality restrictions apply to applications for this project
Supervisors
Primary: Dr Emmanouil Kakouris (Engineering)
Dr Peter Brommer (Engineering)
Project Partner: AWE-NST
This project offers the chance to explore one of the most fascinating challenges in modern engineering, i.e. understanding how and why metals fail under extreme conditions [1, 2, 3]. As a PhD researcher, you will:
- Develop and test novel computer models to reveal how damage builds up and leads to fracture in metals.
- Combine scientific machine learning with data from atomistic and continuum simulations to build faster and more accurate predictive models.
- Apply your observations to guide the design of safer, stronger materials that can perform reliably in demanding real-world environments.
[1] Zhang et al., J. Mech. Phys. Solids 172 (2023) 105186, https://doi.org/10.1016/j.jmps.2022.105186
[2] Saha et al., Comput. Methods Appl. Mech. Eng. 448 (2026) 118493, https://doi.org/10.1016/j.cma.2025.118493
[3] Yin et al., Int. J. Impact Eng. 183 (2024) 104803, https://doi.org/10.1016/j.ijimpeng.2023.104803
By the end of this PhD, you will have:
- Uncovered new insights into how metals behave and fail under extreme conditions, advancing scientific understanding and shaping the future of predictive material modelling and materials research.
- Delivered a software tool that combines high-fidelity simulations with fast scientific machine learning-based predictions to study and improve the design of safer, more resilient materials for real-world engineering challenges today and in the future.
Through this PhD, you will gain advanced expertise and transferable skills for careers in academia, research, or industry, while joining the collaborative HetSys community at the forefront of computational science and engineering. You will:
- Master advanced techniques in computational modelling, scientific machine learning, and data-driven materials science.
- Gain practical experience using high-performance computing and developing scientific software to solve real-world challenges.
- Build strong communication, leadership, and collaboration skills through interdisciplinary training with HetSys, preparing you to become a confident innovator and future leader in your field.
These skills position you for careers in AI research, computational materials science, national laboratories, tech industry or academic research. The HetSys training provides a foundation for these skills through dedicated courses and cohort activities.
We require at least a II(i) honours degree at BSc or an integrated masters degree (e.g. MPhys, MChem, MSci, MEng etc.) in a physical sciences, mathematics or engineering discipline. We do not accept applications from existing PhD holders.
If you are an overseas candidate please check here that you hold the equivalent grades before applying.
For postgraduate study in HetSys, the term “overseas” or “international” student refers to anyone who does not qualify for UK home fee status. This includes applicants from the European Union (EU), European Economic Area (EEA), and Switzerland, unless they hold settled or pre-settled status under the UK’s EU Settlement Scheme.
If you are a European applicant without UK residency or immigration status that qualifies you for home fees, you will be classified as an overseas student.