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undertaken in a hybrid manner. We are also open to discussing flexible working arrangements. As a Lecturer in Teaching and Scholarship, your role will be to support the Lifelong Learning Centre in delivering
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a medical diagnosis based on a similarly spurious rule such as visible marks left by medical previous treatments . It is therefore difficult to ensure that the models we train are reliable
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PhD Studentship in: Understanding risks from emerging contaminants (PFAS) to surface water resources
potential effects on human health. PFAS are frequently detected in various environmental media (including surface water and groundwater) and biological tissues. There is a therefore a need to develop the
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of this research will contribute to the growing field of causal reasoning/inference in computer vision. The primary objective of this research is to develop novel techniques for learning causal representations from
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mechanisms, while at the macro scale, the engineering issue is to develop an efficient system for integrating these metamaterials with several engineering applications. Metamaterials derive their properties
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will be used to train predictive models to inform pro-active network maintenance with the outcomes being reviewed and annually updated. This project offers a unique chance to develop data-driven
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the UKRI rate (£19,237 for 2024/25). The start date is October 2024. The Robotics for Extreme Environments Group invites applications for a PhD studentship to develop (semi-)autonomous control of manipulator
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will include a CPD project working with the NMP to develop its interpretation strategy based on community voices. Eligibility You are required to have a good honours degree (1st Class or an Upper 2:1
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for geographic data integration, existing approaches remain computationally intensive and do not reach the generalisation capabilities obtained from recent AI advances. Key Objectives: Develop robust algorithms
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, the process of recovering real-space images remains unclear due to the inherent and currently intractable complexity of deep learning methods. In this project you will develop Physics-Aware Super-Resolution