Advanced CFD, Multiphysics Modelling, and AI for Energetics Processing

Postgraduate Studentships; Birmingham; Remote; GBP 23,000 - 28,000; Full time

Benefits offered by this job

Funding incl. stipend and fees covered

Job summary

University of Birmingham invites applications for a funded PhD project on Advanced CFD, Multiphysics Modelling, and AI for Energetics Processing, focusing on RAM.

The student will develop high-fidelity CFD models, apply AI-driven optimisation, and validate results with Birmingham's RAM facility. Funding covers tuition and a tax-free stipend of £25k, with an additional industry top-up.

Qualifications

• Strong background in CFD and multiphysics modelling.
• Experience with AI-driven optimisation is desirable.
• Ability to work across computational and experimental validation.

Responsibilities

• Develop high-fidelity RAM CFD models.
• Apply AI-driven optimization to RAM design space.
• Coordinate with experimental validation and scale-up efforts.

Skills

CFD modelling

Multiphysics

AI/ML

Python

Education

PhD in relevant field

Tools

COMSOL

MATLAB

Job description

Advanced CFD, Multiphysics Modelling, and AI for Energetics Processing

This project will develop advanced computational and experimental tools to support the safe, efficient, and scalable manufacture of materials critical to UK and European security, with a particular focus on resonant acoustic mixing (RAM) - a relatively novel and increasingly important class of process equipment attracting growing industrial interest across multiple sectors.
At its core, the project will focus on the development and application of computational fluid dynamics ( CFD ), multiphysics modelling , and AI -driven optimisation for RAM systems. These approaches will be used to better understand, predict, and improve the behaviour of complex material for mulations under RAM conditions, helping to establish a more rigorous scientific and engineering basis for this emerging technology in demanding industrial settings.
The student will explore a number of interconnected research strands, including:
1. Advanced CFD and multiphysics modelling
Development of high-fidelity numerical models of RAM processes, combining fluid and particulate mechanics with relevant physical and chemical phenomena such as temperature evolution, rheological change, and cure behaviour. These models will generate new insight into a class of equipment that remains comparatively underexplored from a modelling perspective, while helping identify operating conditions that improve safety, robustness, and performance.
2. AI -enabled process and equipment optimisation
Application of artificial intelligence, surrogate modelling , and optimisation methods to accelerate exploration of RAM design and operating space. By coupling simulation with AI , the project will support smarter process design, reduce reliance on empirical trial- and -error approaches, and open a route toward more efficient and more readily optimised RAM-based manufacturing systems. The student will also gain access to proprietary AI optimisation workflows developed at the University of Birmingham.
3. Scale-up modelling and experimental validation
Translation of modelling insight from laboratory to industrially relevant RAM systems, supported by access to Birmingham’s National RAM facility, including the world’s only publicly accessible pilot-scale OmniRAM system. This will enable the development and validation of predictive digital frameworks for scale-up, reducing the cost, time, and risk associated with experimental iteration.
A particular strength of the project is the opportunity to combine advanced modelling with unique experimental validation capabilities at Birmingham, including its unique-in-Europe Positron Emission Particle Tracking (PEPT) facility. This will provide uniquely high-resolution ground-truth data on particle and flow behaviour within RAM systems, supporting validation of CFD and multiphysics simulations and , where appropriate, training of AI -based predictive models.
The successful candidate will develop a highly valuable combination of advanced simulation, AI , and experimental validation skills. On the computational side, they will gain expertise spanning CFD , multiphysics simulation, reduced-order and surrogate modelling , symbolic regression, and AI -driven optimisation. On the experimental side, they will gain experience in characterisation and process diagnostics relevant to complex for mulations and process monitoring.
The project will be conducted in close collaboration with a UK-based defence company, ensuring strong industrial relevance and a clear route to impact. The candidate will contribute to challenges of critical national and international importance, while helping advance the modelling , optimisation, and industrial deployment of an increasingly important emerging process technology.

Funding notes:
The successful candidate will receive a full EPSRC stipend, plus an additional £5k top-up from the industrial sponsor, equating to a tax-free annual income of £25k. All fees for the PhD will also be covered by the sponsor, with additional funding to support travel and other expenses.

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