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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 London 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 London
✨Tip Number 1
Network like a pro! Reach out to people in your field, especially those connected to the SMART-HEAT PRO project. Attend relevant events or webinars and don’t be shy about introducing yourself – you never know who might help you land that dream role!
✨Tip Number 2
Showcase your skills! Create a portfolio or a personal website where you can display your projects, especially any related to AI, machine learning, or materials science. This gives potential employers a tangible look at what you can bring to the table.
✨Tip Number 3
Prepare for interviews by brushing up on your knowledge of aluminium alloys and heat-treatment processes. Be ready to discuss how your experience aligns with the responsibilities of the role, particularly in developing machine-learning algorithms and conducting experimental trials.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows you’re genuinely interested in joining us at Brunel University London and contributing to innovative projects like SMART-HEAT PRO.
We think you need these skills to ace Research Assistant/Fellow - Artificially Intelligent Enabled Material Processing in London
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 make it personal – we love seeing your enthusiasm!
Showcase Your Skills: Don’t forget to showcase your technical skills in materials characterisation and data-driven methods. Mention specific techniques you've used, like SEM or machine learning algorithms, to demonstrate your expertise. We’re keen on seeing how you can bring value to our team!
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 Law School
✨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.
✨Showcase Your AI Skills
Since this position involves machine learning and data-driven methods, prepare to talk about any relevant projects or experiences you've had in AI/ML. Highlight how you've applied these skills in practical scenarios, especially in materials science or manufacturing.
✨Collaborative Spirit
This role requires working closely with both academic and industrial partners. Be prepared to share examples of how you've successfully collaborated in the past, and demonstrate your ability to bridge the gap between different fields.
✨Prepare for Technical Questions
Expect some technical questions related to physics-informed machine learning and process optimisation. Review key concepts and think about how you would approach real-world problems using these techniques. Being able to articulate your thought process will impress the interviewers.