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next-generation machine learning (ML) models that are both data-efficient and transferable, enabling more reliable catastrophic risk prediction, defined as the probability of exceeding critical safety
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platform for validating simple quantum devices by measurements that directly reflect the underlying quantum state [1]. What is currently missing is a theoretical framework for validating the quantum models
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level, with sub-metre precision? This PhD will develop next-generation foundation-model-driven urban perception systems that fuse: high-resolution satellite imagery, aerial data, street-level imagery (e.g
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integrating machine learning, computational modelling, and experimental validation. The successful candidate will receive training in both computational and experimental biology within a highly collaborative
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bottlenecks to scalability by reducing the number of operations, enhancing the robustness of gates, and mitigating the effects of noise. The research will upon strengths in the quantum control modelling
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modelling, statistical inference, and machine-learning techniques, with potential applications well beyond nuclear science, including data science and AI-related fields. Expertise in nuclear data and
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include data analysis and there is scope for modelling of the swimmers. The ideal candidate will have a passion for interdisciplinary research at the intersections of physics and biology. Due
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applications where labelled data is scarce, enabling models to learn from the data itself without relying on extensive human annotation. Supervisors: Dr Donya Hajializadeh, Dr Fernando Madrazo-Aguirre, Dr Sara