About this role
Location: Brunel University London, Uxbridge Campus
Salary: Grade R1
Research Assistant: from £36,640 to £38,638 inclusive of London Weighting with potential to progress to £39,682 per annum inclusive of London Weighting through sustained exceptional contribution.
Research Fellow: from £40,757 to £44,179 inclusive of London Weighting with potential to progress to £52,067 per annum inclusive of London Weighting through sustained exceptional contribution.
Hours: Full-time
Contract Type: Fixed term 10 months
Brunel University London was established in 1966 and is a leading multidisciplinary research-intensive technology university delivering economic, social and cultural benefits. For more information please visit: https://www.brunel.ac.uk/about/our-history/home
BCAST is a world-leading research centre in solidification science and lightweight metals, offering state-of-the-art facilities including advanced microscopy, thermal processing systems, and digital modelling capabilities. The centre has a strong track record in industrial collaboration and technology translation. For more information, please refer to: https://www.brunel.ac.uk/research/Centres/BCAST
The Role
We are seeking a highly motivated Research Assistant/Fellow to join BCAST at Brunel University London, contributing to the Innovate UK-funded SMART-HEAT PRO project. This project is developing a scalable, AI-enabled digital platform to transform industrial heat treatment processes, improving energy efficiency, reducing scrap, and enabling real-time optimisation through machine learning and advanced sensor integration.
The successful candidate will play a key role in bridging materials science and data-driven modelling, supporting the development of physics-informed machine learning approaches for aluminium alloy heat treatment. The key responsibilities include, but not limited to:
Design and conduct experimental heat treatment trials for aluminium alloys, generating high-quality datasets for model development Develop and enhance machine learning algorithms, software and system for heat treatment process Perform advanced materials characterisation (e.g. SEM, hardness testing, microstructural analysis) to validate process outcomes Validation of physics-informed machine learning models for process optimisation Collaborate with industrial partners to integrate metallurgical insights into real-time control systems Contribute to the development of a digital materials knowledge base linking process parameters to performance outcomes Prepare technical reports, publications, and presentations for both academic and industrial audiences
The candidate
You will have:
A PhD or relevant degree in Materials Science, Metallurgy, Mechanical, Computer Engineering, or a related discipline Strong knowledge of aluminium alloys and heat treatment processes Experience in materials characterisation techniques (e.g. SEM, EBSD, mechanical testing) Interest or experience in data-driven methods, machine learning, or digital manufacturing Ability to work collaboratively across academic and industrial environments
Desirable:
Experience in AI/ML applied to materials or manufacturing Familiarity with digital twin concepts or process modelling Experience working on collaborative R&D or Innovate UK projects
We offer a generous annual leave package plus discretionary University closure days, excellent training and development opportunities as well as a great occupational pension scheme and a range of health-related support. The University is committed to a hybrid working approach.
Closing date for applications: 25 May 2026
If you have any technical issues, contact us at: [email protected].
Brunel University London has a strong commitment to equality, diversity and inclusion. Our aim is to promote and achieve a fully inclusive workforce to reflect our community.