Closing Date
Friday 28 February 2025
Reference
SCI02025
Project Overview
Project: AI-Driven infrastructure inspection: continual improvement through reinforcement learning.
A three-year studentship, beginning in October 2025. 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 the Faculty of Science, University of Nottingham, which conducts cutting-edge research into the development of novel causal inference, automation, and robotic control algorithms.
Role Overview
We are seeking a highly motivated PhD student to conduct cutting-edge research on AI techniques and reinforcement learning, a technology that 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 automated annotation and diagnostic software of critical infrastructure via continual learning from sensor feedback.
Research Goals
This PhD aims to develop novel algorithms for Artificial Intelligence (AI) driven continual learning via reinforcement learning (RL), incorporating mechanistic knowledge through causal inference constraints. These algorithms will enable adaptive digital systems for assisted and automated annotation software, as well as diagnostics software, particularly in the contexts of manufacturing and precision imaging. By leveraging causal insights, the project will enhance the systems' ability to learn dynamically from sensor feedback while maintaining consistency and reliability. In the later stages, the research will apply these advancements to a case study on adaptive disassembly lines, demonstrating how continual learning can drive more efficient and sustainable solutions in complex, evolving environments.
Team and Supervision
You will have the opportunity to join a multidisciplinary team of supervisors: experts in engineering and biochemistry related to different battery technologies; experts in foundational computer science and the mathematical foundations of AI; and experts in the industrial utilization of emerging AI technologies for various manufacturing and built environment inspection processes.
Funding
This 3-year fully funded studentship is 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 sponsor's own security checks prior to the commencement of the PhD.
Application Process
For full details please see the project information page: Project Information Page.
For informal enquiries, please send a detailed CV and academic transcripts to Dr Yordan Raykov and Dr Yazan Qarout.
Deadline for applications: 28 February 2025.
#J-18808-Ljbffr