PhD Studentship: A Continual Learning Approach for Robust Robotic Control in Electric Batteries Assembly

This project is an exciting opportunity to undertake industrially linked research in partnership with the Manufacturing Technology Centre (MTC). It is based within the Advanced Manufacturing Technology Research Group (AMTG) at the Faculty of Engineering, University of Nottingham (UoN), which amongst its wide research portfolio, conducts cutting edge research into the development of future Intelligent Reconfigurable Manufacturing Systems.

This is 3-year fully funded studentship and is only open to UK home students. The successful applicant will receive a generous tax-free annual stipend of £25,000 plus payment of their full-time home tuition fees. Additionally, £2,000 per annum is provided for consumables, travel, etc. Due to funding restrictions this PhD position is only available to UK nationals. As this position is sponsored by the MTC, any successful candidate would need to pass the sponsors own security checks prior to the commencement of the PhD.

Motivation

Automation is key to meeting the growing demand for Electric Vehicle (EV) batteries. Highly reconfigurable robotic cells powered by AI promise to deliver the new generation of resilient manufacturing systems. However, real applications of AI have typically been demonstrated under highly controlled conditions. Battery assembly processes can be inherently dangerous demanding very precise assembly tasks, and so flexible automation solutions need to be safe and accurate.

Aim

This project will focus on investigating and developing new ways in which deep learning-based solutions can continuously learn and deal with unseen situations, with a particular focus on robotic control. Building from existing concepts on continual learning, this project will develop new frameworks for training and maintaining robustness and stability of deep learning models, especially when new training experiences are corrupted. The framework will be validated in robotic control scenarios during EV battery assembly.

As a PhD student, you will work with both academics from the AMT Group at UoN as well as the engineering teams within the MTC. This will give you real-world experience in working within in an industrial company, as well as experiencing the workplace and culture within it.

Who we are looking for

We are looking for an enthusiastic, self-motivated candidate, with a 1^st or high 2:1 degree in computer science or mechanical engineering. The candidate will have programming experience, particularly on the development of machine learning pipelines.

The University actively supports equality, diversity and inclusion and encourages applications from all sections of society. The Faculty of Engineering (FoE) provides a thriving working environment for all Postgraduate Researchers (PGRs) creating a strong sense of community across research disciplines.

The MTC is an independent Research and Technology Organisation (RTO) aimed at de-risking and accelerating the adoption of disruptive technologies within the UK manufacturing sphere. Supported by the UK government, the MTC works closely with industrial partners and other research organisations to deliver world leading innovation across all levels of the UK’s industrial landscape. For more information, please visit the MTC website.

Contact

For further information please contact Dr Giovanna Martinez Arellano (giovanna.martinezarellano@nottingham.ac.uk).

Closing Date: 30^th June 2025

Start Date: 1st October 2025

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