Research Assistant/Fellow in Digital Twin for Advanced Manufacturing - 16864

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

Brunel University London is seeking a highly motivated Research Assistant/Fellow (PDRA) to join BCAST, a leading UK research centre in advanced materials and manufacturing. You will play a key role in the DIGI-HSMC project, an ambitious collaborative programme developing next-generation High Shear Melt Conditioning (HSMC) systems enhanced with digital twin technology, real-time sensing, and intelligent process control.

This is an opportunity to work at the intersection of metallurgy, digital manufacturing, and industrial innovation, with strong engagement with industry partners. The key responsibilities include, but not limited to:

• Develop and implement digital twin models for molten metal processing
• Integrate and analyse data from various sensors such as temperature, torque, and acoustic sensors
• Perform advanced materials characterisation (e.g. SEM, hardness testing, microstructural analysis) to validate process outcomes
• Support development and validation of physics-informed machine learning models for process optimisation
• Support experimental trials and validation in laboratory and industrial environments
• Collaborate with academic and industrial partners to refine system performance
• 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, Manufacturing, or a related discipline
• Experience in at least two of the following:
  • Digital twins or process modelling
  • Sensor systems and data acquisition
  • Metallurgy, casting, or thermal processing
  • Strong programming skills (e.g. Python, MATLAB, LABVIEW or similar)
  • Interest or experience in data-driven methods, machine learning, or digital manufacturing
  • Ability to work across experimental and computational environments
  • Excellent communication and teamwork skills

Desirable:

• Experience in AI/ML for industrial processes
• Knowledge of metal casting or melt processing

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.

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