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are developed with optimized computing architectures beyond classical approaches, To model and improve scalability and intrinsically integrate AI self-learning capabilities within the material at large-scale
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the next 18 months. By integrating data pre-processing and machine learning algorithms inspired by the Tsetlin Machine principles, the project will develop tools to generate scalable hardware architectures
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PhD Studentship: How Does Spatial Organisation Impact Host-microbiome Interactions in Human Airways?
of lengths 50 µm, 500 µm, and 1000 µm. Following cell seeding, the colonies will be analysed for morphology and architecture (via confocal imaging), release of inflammatory cytokines (via ELISA, qPCR), and
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Applications are invited for a Research Assistant or Research Associate to work on efficient machine-learning systems for earth observation. The post holder will be part of the Computer Architecture
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software and demonstrate best practices for method/equations/formulation selection for emerging architectures of Exascale High Performance Computing (HPC) facilities. The student involved in this project
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architecture integration. The current motor, inverters and controllers do not account for interactions between sub-systems and, as a result, key attributes such as efficiency do not reach the full potential
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The push for net-zero power and propulsion technologies is leading to many new device architectures being proposed. Within these radial and mixed flow pumps and compressors feature heavily, often in
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Supervisory Team: Prof Johan Nilsson, Dr William Kerridge-Johns Project description: This project, focussing on high-power laser architectures, will contribute to a major Ministry of Defence (MoD
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Science, this project will look at the explainability of AI-based biometrics systems. The project will: Scrutinise the performance of open deep-learning architectures in facial and voice biometric modalities, assessing
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on operations. To support development of complex multi-layered systems and to inform rapid decision-making, this project will develop advanced Machine Learning (ML) architectures that learn directly from a