PhD Studentship: Modelling, AI-driven Optimisation and Experimental Validation for Safer and Smarter Processing of Defence Materials

This project will develop cutting-edge tools and methodologies to support the safe, efficient, and scalable manufacture of materials critical to the UK and European security, through the use of resonant acoustic mixing (RAM) – a relatively novel processing methodology which is of increasing industrial interest spanning multiple sectors.

The project will allow the candidate to explore a number of related research strands, including:

1. RAM Process Modelling: The development of advanced numerical models for RAM processes, incorporating complex phenomena such as chemically induced viscosity changes, temperature evolution, and cure kinetics. These models will allow prediction and optimisation of RAM parameters, and stand to revolutionise current modelling frameworks and production methods.
2. Geometry and Surface Finish Effects: Systematic investigation of how vessel shape and internal surface characteristics affect mixing performance. By combining experimental work with simulation and AI-driven optimisation, the project aims to derive generalised design rules to improve both effectiveness and efficiency in material processing.
3. Scale-Up Modelling and Validation: Bridging laboratory-scale insights to industrial-scale RAM systems, thanks to access to Birmingham’s National RAM facility – including the world’s only publicly-accessible pilot-scale OmniRAM system. This work will develop and validate predictive tools to minimise the need for repeated scale-up trials, reducing cost and time while enhancing safety.

The successful candidate will be strongly supported in gaining deep expertise in diverse and highly transferable skills, including rheology, spectroscopy, particle characterisation, and advanced imaging techniques, including rare access to unique capabilities found only at the University of Birmingham, such as industrial-scale Positron Emission Particle Tracking, and cutting-edge Terahertz Raman spectroscopy.

On the computational side, they will develop and apply a broad range of highly-transferrable digital tools, from high-fidelity simulations to symbolic regression and AI-driven optimisation methods, again including unique frameworks developed at the University. These capabilities are in high demand across multiple sectors, and as such will arm the successful candidate with a track record and skill set placing them ideally for rapid advancement in their later career.

The project will be conducted in close collaboration with a UK-based Defence company, ensuring strong industrial relevance and the opportunity for direct impact. The candidate will contribute to addressing challenges of critical national and international importance, with clear pathways to uptake within the UK’s sovereign capability frameworks.

To apply, please contact Prof. Kit Windows-Yule (c.r.windows-yule@bham.ac.uk).

Funding notes:

This PhD will be fully-funded, and include a tax-free stipend of £25,000 per annum.

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