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: hrsystems@brunel.ac.uk.

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.