|£38,205 to £41,732
|8th December 2023
|7th January 2024
Founded in 1894, City, University of London is a global university committed to academic excellence with a focus on business and the professions.
City attracts around 20,000 students (over 40% at postgraduate level), from more than 150 countries and staff from over 75 countries.
In the last decade City has almost tripled the proportion of its total academic staff producing world-leading or internationally excellent research. During this period City has made significant investments in its academic staff, its estate and its infrastructure and continues to work towards realising its vision of being a leading global university.
City, University of London (City) and Airborne Composites Ltd (Airborne) are collaborating on a 24-month project to apply machine vision, sensor technology, machine learning and artificial intelligence tools and methodologies to optimise a composite manufacturing production system which focuses on niche and customised manufacture. Academic supervision will come from Dr Hamed Yazdani Nezhad, from the University of Leeds.
The Associate will be based at Airborne’s manufacturing site at Membury Airfield, Berkshire, visiting the Departments of Engineering at City and Leeds as required. Airborne is a rural location so ability to travel by car to/from work would be beneficial.
Airborne produces high-end thermoset and thermoplastic composite parts (e.g. aircraft components, car roof panels), and develops fully automated manufacturing production lines.
The candidate will drive this KTP partnership, working with academic and company partners, ensuring key objectives are met. The Associate will create, integrate, and embed a suite of technologies and tools optimising the existing composites manufacturing line at Airborne.
The Associate will focus on process mapping, non-contact thickness detection for carbon preforms, 3D geometry detection and visualisation, preform quality evaluation and energy optimisation via ML/AI, and benefit evaluation via Discrete Event Simulation.
The Associate will need to understand the processes of manufacturing composite parts from composites materials and material properties. They will create a library of automation processes to be implemented on a manufacturing line within Airborne or exploited commercially.
The candidate will ideally have knowledge of a variety of technologies and digital tools including robot platforms, servo and motor controls, 3D cameras, machine learning, computer vision and distributed systems.
In addition, the preferred ideal candidate will have knowledge of software frameworks such as Django, MVVM and software stack for live data communication.
City offers a sector-leading salary, pension scheme and benefits including a comprehensive package of staff training and development.
Closing date: 2nd January 2024 at 11:59pm.
City, University of London is committed to promoting equality, diversity and inclusion in all its activities, processes, and culture for our whole community, including staff, students and visitors.
We welcome applications regardless of age, caring responsibilities, disability, gender identity, gender reassignment, marital status, nationality, pregnancy, race and ethnic origin, religion and belief, sex, sexual orientation and socio-economic background.
City operates a guaranteed interview scheme for disabled applicants.
The University of business, practice and the professions.
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