Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Conduct experiments and develop AI algorithms for advanced materials processing.
- Company: Brunel University London, a leader in innovative research.
- Benefits: Gain hands-on experience, work with cutting-edge technology, and enhance your skills.
- Other info: Collaborative environment with opportunities to publish and present your work.
- Why this job: Join a pioneering project that transforms industrial processes and boosts sustainability.
- Qualifications: PhD or relevant degree in Materials Science or Engineering; knowledge of aluminium alloys required.
The predicted salary is between 35000 - 45000 £ 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 research projects
Research Assistant/Fellow - Artificially Intelligent Enabled Material Processing 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
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend relevant events, and connect with professionals on LinkedIn. We can’t stress enough how important it is to make those connections; you never know who might help you land that dream job!
✨Tip Number 2
Prepare for interviews by researching the company and the specific project you'll be working on. Familiarise yourself with the SMART-HEAT PRO project and think about how your skills in materials science and machine learning can contribute. We want you to shine when you get that chance to impress!
✨Tip Number 3
Showcase your skills through practical examples. Whether it's a project you've worked on or a problem you've solved, we want to see how you apply your knowledge in real-world scenarios. Bring your A-game to demonstrate your expertise in aluminium alloys and heat treatment processes!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining our team at Brunel University London. Let’s make it happen together!
We think you need these skills to ace Research Assistant/Fellow - Artificially Intelligent Enabled Material Processing
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 relevant machine learning projects. We want to see how your background fits into our exciting work at BCAST!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about AI-enabled material processing and how you can contribute to the SMART-HEAT PRO project. Let us know what excites you about working with us!
Showcase Your Skills: Don’t forget to showcase your skills in materials characterisation and data-driven methods. If you've got experience with SEM or machine learning algorithms, make sure to mention it! We love seeing candidates who are ready to dive into hands-on research.
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. Plus, you’ll find all the details you need about the role and our team!
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 specific techniques like SEM and mechanical testing, as well as how they relate to the role. This shows you're not just interested in the position but also have a solid grasp of the technical aspects.
✨Showcase Your Projects
Prepare to talk about any relevant projects you've worked on, especially those involving machine learning or data-driven methods. Highlight your contributions and the outcomes, as this will demonstrate your hands-on experience and problem-solving skills that are crucial for the SMART-HEAT PRO project.
✨Collaborative Spirit
Since the role involves working with industrial partners, be ready to discuss your experience in collaborative environments. Share examples of how you've successfully worked in teams, particularly in academic or industrial settings, to show that you can integrate metallurgical insights into real-time control systems.
✨Prepare Questions
Have a few thoughtful questions prepared about the project and the team at BCAST. This not only shows your genuine interest but also gives you a chance to assess if the role and environment are the right fit for you. Ask about their approach to integrating AI/ML in materials processing or how they envision the digital materials knowledge base evolving.