Research Assistant - 16974
You will join the Brunel Digital Manufacturing Research Centre, Department of Engineering, College of Engineering, Design and Physical Sciences at Brunel University.
Location: Brunel University London, Uxbridge Campus
Salary: Grade R1 from: £37,118 to £39,144 per annum inclusive of London Weighting with potential to progress to £40,202 per annum inclusive of London Weighting through sustained exceptional contribution. (Pro-rata for Part-time)
Hours: Part-time at 25.31 hours per week
Contract Type: Fixed-term for 6 months
You will join the Brunel Digital Manufacturing Research Centre, Department of Engineering, College of Engineering, Design and Physical Sciences at Brunel University.
SME manufacturers lose between 2% and 5% of output through scrap and rework, driving unnecessary material consumption, machine hours, and energy use. The Lean Optimal feasibility study will investigate whether a simple, affordable and scalable predictive system can help SMEs identify this drift by making use of the production data they already collect.
The project explores an approach that combines lightweight AI-assisted modelling, drift detection and basic self-learning behaviour with operator guidance. The aim is not to build a full system but to establish whether predictive insight, early warnings and actionable recommendations can be generated within the constraints of real SME environments. This includes assessing data readiness, understanding operational workflows and determining how predictions and guidance can be presented in a form that supports decision making without disrupting production.
The concept builds on earlier research from the Factory of the Future programme, where predictive modelling, integrated information systems and automated warning mechanisms formed the basis of the Lean Optimal philosophy.
The candidate will have significant experience working in R&D and/or industrial set up to support the design and development of advanced data acquisition, analytics and predictive modelling using novel AI and Machine Learning techniques for optimising manufacturing processes with lean objectives.
This highly applied short-term research project requires highly skilled individuals requiring minimum time to utilise their capabilities to think innovatively and produce solutions that will be applied and tested in limited amount of time with demonstrable output (in the form of software application and 1 peer reviewed publication).
The educational background should be in areas such as Electronics, Computer Science, Applied Mathematics, Manufacturing Systems with Machine Learning-Data Analytics orientation. This six-months period is to assist the existing research team to finalise the models and code the solution into a software application. The nominee should be expert in these areas and type of projects.
The candidate main role is to support the existing SERG members with further testing and validation of models as well as coding the solutions into an integrated software application.
Post Profile
• This position requires strong computation and software development, mathematical and analytical skills supported by evidence of your previous activities and responsibilities
• Technical and engineering skills to develop a concept, apply the design methodologies, implementation, testing, validation and verification
• Strong Communication and Writing Skills to write scientific and technical reports.
Management of Staff and Students
• To provide appropriate advice to staff and students on their research area or research methodologies
Effective Behaviours
• Timeliness
• Meeting Deadlines
• Communication and Networking
• Networking group across colleges
• Ability to negotiate and influence
• Ability to plan and organise own workload
• Ability to adapt to a flexible approach to the demands of a busy college/department in order to accommodate changes in priorities when required
Closing date: 1 October 2026
To apply please visit https://careers.brunel.ac.uk
Brunel University London has a strong commitment to equality, diversity and inclusion.