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- Tasks: Join a cutting-edge project to optimise industrial heat treatment using AI and machine learning.
- Company: Brunel University London, a leader in innovative research and technology.
- Benefits: Generous leave, training opportunities, hybrid working, and a supportive work environment.
- Other info: Collaborate with industry partners and contribute to groundbreaking research in AI-enabled processes.
- Why this job: Make a real impact in materials science while advancing your career in a dynamic setting.
- Qualifications: PhD or relevant degree in Materials Science or related fields; experience in machine learning is a plus.
The predicted salary is between 36640 - 38638 £ per year.
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.
Research Assistant/Fellow - Artificially Intelligent Enabled Material Processing in Uxbridge employer: Brunel Law School
Contact Detail:
Brunel Law School 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 current or former employees at Brunel University London, especially those in BCAST. A friendly chat can give us insider info and maybe even a referral!
✨Tip Number 2
Prepare for the interview by brushing up on your knowledge of aluminium alloys and heat-treatment processes. We want to show that we’re not just passionate but also knowledgeable about the field.
✨Tip Number 3
Showcase our skills in machine learning and data-driven methods during the interview. Bring examples of past projects or experiences that highlight our expertise in these areas.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure our application gets noticed and shows that we’re serious about joining the team at Brunel.
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 Research Assistant/Fellow role. Highlight your experience with aluminium alloys, heat treatment processes, and any relevant machine learning projects. We want to see how your background fits perfectly 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 skills can contribute to our goals. Keep it engaging and personal – we love to see your personality come through!
Showcase Relevant Experience: When detailing your experience, focus on specific projects or roles that relate to materials science and data-driven methods. If you've worked on collaborative R&D projects or have AI/ML experience, make sure to highlight that – it’s super relevant to us!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, it shows us you’re keen and ready to join our team at Brunel University London!
How to prepare for a job interview at Brunel Law School
✨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 SMART-HEAT PRO project.
✨Show Off Your Data Skills
Since this role involves machine learning and data-driven methods, prepare to talk about any relevant experience you have. Bring examples of projects where you've developed algorithms or worked with datasets, and be ready to explain your thought process.
✨Collaborative Spirit
This position requires working with both academic and industrial partners. Think of examples from your past experiences where you successfully collaborated with others. Highlight your ability to communicate complex ideas clearly and work as part of a team.
✨Prepare for Technical Questions
Expect some technical questions related to materials characterisation and machine learning. Practice explaining your approach to problem-solving in these areas, and don’t hesitate to ask clarifying questions if you need more context during the interview.