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PhD Studentship : AI - Driven Battery Disassembly : Adaptive Efficiency Through Reinforcement Learning

University of Nottingham - Science

Qualification Type: PhD
Location: Nottingham, Hybrid
Funding for: UK Students
Funding amount: See advert for details
Hours: Full Time
Placed On: 7th February 2024
Closes: 15th March 2024
Reference: SCI257

This project is an exciting opportunity to undertake industrially linked research in partnership with the Manufacturing Technology Centre (MTC). It is based within the School of Mathematical Sciences at University of Nottingham which conducts cutting edge research into machine learning methods for inverse modelling problems. 

This is 3-year fully funded studentship is only open to UK home students. The successful applicant will receive a generous tax-free annual stipend of £26,658 plus payment of their full-time home tuition fees. 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 a motivated PhD candidate with enthusiasm to learn about state-of-the-art developments in statistical machine learning and reinforcement learning, a technology which has powered many of the recent groundbreaking self-guided game engines and large language models. Together we will study how the existing and emerging paradigms in reinforcement learning can be utilized to power robotics systems in autonomously optimizing the battery disassembly process from sensor feedback.

Motivation

The surge in electric vehicles (EVs) has created challenges in battery recycling due to diverse types, sizes, and chemistries. Static automation struggles with constant design changes. As battery demand rises, efficient, adaptable disassembly for recycling is crucial. Efficient disassembly is a prerequisite for recovering valuable materials from spent batteries, such as lithium, cobalt, nickel, and other metals, which can be reused in the production of new batteries. Novel technologies aim to address this by providing flexible, safe solutions for the evolving battery landscape, ensuring effective recycling and resource recovery. This PhD will investigate the implementation of life-long learning artificial intelligence using reinforcement learning (RL) for battery disassembly.

Aim

You will have the opportunity to join a multidisciplinary team of supervisors: experts in engineering and biochemistry related to different battery technologies; experts in foundation computer science and mathematical foundations of AI and experts in the industrial utilization of emerging AI technologies for various manufacturing processes.

You will work alongside a team of research engineers based at the MTC, as well as a vibrant cohort of PhD candidates from the School of Mathematical Sciences at University of Nottingham, the Horizon DTC and the AI DTC and the University of Nottingham.

Who we are looking for

An enthusiastic, self-motivated, PhD candidate with an aptitude for programming and problem solving. The ideal candidate would have (i.e. or expect to have by the start date) a 1st or a 2:1 degree in a STEM field such as Mathematics, Computer Science, Engineering, Physics and others. Prerequisite background in AI or robotics systems would be advantageous but it is not expected. However, we do expect candidates to have adequate experience in coding in at least one object-oriented language (Python, MATLAB, R, C++ etc.).

Applications and enquiries can be made informally directly to the supervisor (Dr. Yordan P. Raykov) at yordan.raykov@nottingham.ac.uk, but post interview application to be made through MyNottingham system.

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