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- Tasks: Conduct innovative research on AI-enabled heat treatment processes and develop machine learning algorithms.
- Company: Brunel University London, a leader in advanced materials research.
- Benefits: Generous leave, hybrid working, competitive salary, and excellent training opportunities.
- Other info: Collaborate with industry partners and contribute to impactful research in a dynamic environment.
- Why this job: Join a cutting-edge project that transforms industrial processes and enhances energy efficiency.
- Qualifications: PhD or relevant degree in Materials Science or Engineering, with knowledge of aluminium alloys.
The predicted salary is between 36640 - 38638 £ per year.
We are seeking a highly motivated Research Assistant/Fellow to join the Brunel Centre for Advanced Solidification Technology (BCAST) at Brunel University London, contributing to the Innovate UK‑funded SMART‑HEAT PRO project – a scalable AI‑enabled digital platform to transform industrial heat treatment processes, improve energy efficiency, reduce scrap and enable real‑time optimisation through machine learning and advanced sensor integration.
Location: Brunel University London, Uxbridge Campus
Hours: Full‑time
Contract Type: Fixed‑term 10 months
Responsibilities:
- 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 systems for the heat treatment process
- Perform advanced materials characterisation (SEM, hardness testing, microstructural 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 both academic and industrial audiences
Qualifications:
- PhD or relevant degree in Materials Science, Metallurgy, Mechanical or 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:
We offer a generous annual leave package plus discretionary University closure days, excellent training and development opportunities, a competitive occupational pension scheme and a range of health‑related support. The University is committed to a hybrid working approach.
Salary:
Research Assistant: £36 640 to £38 638 (inclusive of London weighting), with potential progression to £39 682. Research Fellow: £40 757 to £44 179 (inclusive of London weighting), with potential progression to £52 067.
Closing date: 25 May 2026
Equal Opportunity: Brunel University London has a strong commitment to equality, diversity and inclusion. Our aim is to promote and achieve a fully inclusive workforce that reflects our community.
Research Assistant/Fellow - Artificially Intelligent Enabled Material Processing in Uxbridge employer: Brunel University of London
Contact Detail:
Brunel University of London Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Research Assistant/Fellow - Artificially Intelligent Enabled Material Processing in Uxbridge
✨Tip Number 1
Network like a pro! Reach out to people in your field, especially those connected to Brunel University or the BCAST project. A friendly chat can open doors and give you insights that a CV just can't.
✨Tip Number 2
Show off your skills! If you’ve got experience with machine learning or materials characterisation, don’t just mention it—bring it to life. Share examples of your work or projects during interviews to really impress.
✨Tip Number 3
Prepare for the unexpected! Research the latest trends in AI and materials science, and be ready to discuss how they relate to the role. This shows you're not just interested but also engaged with the field.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who take the initiative to connect directly with us.
We think you need these skills to ace Research Assistant/Fellow - Artificially Intelligent Enabled Material Processing in Uxbridge
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the role. Highlight your experience with aluminium alloys, heat treatment processes, and any machine learning projects you've worked on. We want to see how your skills align with what we're looking for!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about the SMART-HEAT PRO project and how your background makes you a perfect fit. Keep it engaging and relevant to the job description.
Showcase Your Research Skills: Since this role involves conducting experimental trials and developing algorithms, be sure to mention any relevant research experience. We love seeing examples of your work, so don’t hold back on sharing your achievements!
Apply Through Our Website: We encourage you to apply through our website for a smooth application process. It’s the best way to ensure your application gets to us directly and stands out in the crowd!
How to prepare for a job interview at Brunel University of London
✨Know Your Stuff
Make sure you brush up on your knowledge of aluminium alloys and heat treatment processes. Be ready to discuss your experience with materials characterisation techniques like SEM and mechanical testing, as these will be crucial for the role.
✨Show Off Your Skills
Prepare to talk about any machine learning algorithms or data-driven methods you've worked with. If you have experience in AI/ML applied to materials or manufacturing, make sure to highlight specific projects or outcomes that demonstrate your expertise.
✨Collaborate Like a Pro
This role involves working with both academic and industrial partners, so be ready to share examples of how you've successfully collaborated in the past. Discuss any R&D projects you've been part of, especially if they relate to Innovate UK initiatives.
✨Ask Smart Questions
Prepare thoughtful questions about the SMART-HEAT PRO project and how it integrates AI with heat treatment processes. This shows your genuine interest in the role and helps you understand how you can contribute effectively.