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Project Description The global transition toward a net-zero economy requires secure and sustainable supplies of critical minerals, with lithium forming the backbone of electric-vehicle batteries and
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intervention portfolios. Stakeholder preference rankings derived through inverse reinforcement learning and constraints related to compliance, safety, cost, and equity will guide policy learning, ensuring
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: Imagine a surgeon operating remotely through a robot—what if the network slows at a critical moment? Even tiny delays can risk patient safety. This PhD project develops new AI approaches to predict network
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, allowing surgeons to operate on patients from different locations. However, communication delay remains a major safety challenge. Even small or unexpected delays can affect robot responsiveness and
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energy demand. Combining applied mycology, food safety modelling, precision agriculture and Net Zero energy systems, the research will deliver energy-efficient, data-driven grain storage solutions
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pretrained models, where parameter-efficient methods must balance erasure guarantees against model utility Unlearning as an AI safety primitive — exploring how federated unlearning can contribute to removing
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) This PhD project aims to define evidence-based, task-specific colour vision requirements for mainline train drivers and other safety-critical rail staff. The research will focus on colour-dependent visual
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. This work will therefore be crucial in contributing to a new generation of diagnostics that may have implications for food security and conversation. The successful student will explore innovative synthetic
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have implications for food security and conversation. The successful student will explore innovative synthetic biology approaches to develop rapid, low-cost, and field-deployable tests for detecting
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available to cover research and training costs, conference attendance, etc. Due to funding restrictions, applicants not eligible for UK home fee status will only be considered if they can secure additional