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.
Location: Brunel University London, Uxbridge Campus
Salary: Grade R1 – Research Assistant: £36,640‑38,638 per annum (excluding London Weighting), potential progression to £39,682. Research Fellow: £40,757‑44,179 per annum, potential progression to £52,067.
Hours: Full-time
Contract: Fixed term 10 months
The Role
Work on the SMART-HEAT PRO project as a bridge between materials science and data-driven modelling, developing physics-informed machine learning approaches for aluminium alloy heat treatment.
Responsibilities
* Design and conduct experimental heat‑treatment trials for aluminium alloys, generating high‑quality datasets.
* Develop and enhance machine‑learning algorithms, software and systems for heat‑treatment process optimisation.
* Perform advanced materials characterisation (SEM, hardness testing, micro‑structural analysis) to validate process outcomes.
* Validate 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 academic and industrial audiences.
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 (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.
Benefits
Generous annual leave, discretionary university closure days, excellent training and development opportunities, occupational pension scheme, health related support. Hybrid working approach.
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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