PhD Studentship: A continual learning approach for robust robotic control in electric batteries assembly.
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, 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.
Vision
We are seeking for a highly motivated PhD student to conduct cutting edge research of the AI techniques that will power future flexible manufacturing systems. Together, we will make technological advances in more efficient and resilient manufacturing systems that can cope with demands of high value products such as electric batteries. We will also make advances towards the scalability and robustness of AI in complex environments which is a major step towards the digital transformation of the manufacturing industry.
Motivation
Automation is key to meeting the growing demand for Electric Vehicle (EV) batteries. However, traditional automation systems require significant investment in redesign or modification to accommodate product variability or volume change. Highly reconfigurable robotic cells promise to deliver the new generation of manufacturing systems that will enable more flexible and resilient production. AI is expected to be at the centre of these systems, being the foundation of computer vision, monitoring, and control solutions. However, real applications of AI have typically been demonstrated under highly controlled conditions. Battery assembly processes can be inherently dangerous (high-voltage levels, critical temperature management) demanding very precise assembly tasks, and so flexible automation solutions need to be safe and accurate. Until now, it is not well understood how such automation solutions can be safely and robustly supported with state-of-the-art deep learning. There is a need for new AI that can incrementally learn and adapt without losing accuracy to support safe automation.
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 some of the existing concepts on continual learning, the aim of this project will be to 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, under process variations such as battery re-design and facility layout change.
Both Omnifactory and the MTC have facilities that aim to enable the UKs long term strategy in electrification, providing a highly-automated battery assembly and disassembly environment for recovery of critical raw materials, key to securing a circular supply chain to support a UK battery industry.
As a PhD student, you will work with both academics from the AMT Group at University of Nottingham and also have the opportunity to work with the engineering teams within the Manufacturing Technology Centre (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 1st 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. Community and research culture is important to our PGRs and the FoE support this by working closely with our Postgraduate Research Society (PGES) and our PGR Research Group Reps to enhance the research environment for PGRs. PGRs benefit from training through the Researcher Academy’s Training Programme, those based within the Faculty of Engineering have access to bespoke courses developed for Engineering PGRs. including sessions on paper writing, networking, and career development after the PhD. The faculty has outstanding facilities and works in partnership with leading industrial partners.
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, from SMEs and start-ups to OEMs and large-scale global manufacturers. For more information please visit the MTC website.
Contact
For further information on this PhD position please contact Dr Giovanna Martinez Arellano(giovanna.martinezarellano@nottingham.ac.uk).
Closing Date: 30th June 2025.
Proposed PhD Start Date: 1st October 2025.
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