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Field
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combining physical models, sensor data, computational methods, and damage and fracture mechanics concepts to create a virtual replica of the composite tank, enabling predictive maintenance, lifetime
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provide large and complex datasets. By applying advanced pattern recognition and clustering algorithms, the aim is to automatically detect coherent spatial domains. These domains represent regions with
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-binding domain and leucine-rich repeat (NLR) genes play important roles as the sensors/receptors of non-self molecules and in activation of immune responses such as transcriptional reprogramming and cell
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approach that integrates machine learning algorithms, blockchain technology, and IoT devices with digital twin systems. The scientific objectives of the project are as follows: Objective 1: Investigate how
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industries: in-car systems, medical devices, phones, sensor networks, condition monitoring systems, high-performance compute, and high-frequency trading. This CDT develops researchers with expertise across
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creating robust, low cost, and real-time edge-AI algorithms capable of accurately classifying diverse marine species and debris under complex and dynamic underwater conditions. The demand for such a low-cost
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Researcher will influence the direction of application areas and algorithm development, receiving direct training in InSAR processing, geospatial data science, and agricultural remote sensing. Co-supervision
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embedded in soft bodies. These oscillators - recently demonstrated as multifunctional units that can simultaneously act as valves, sensors, and actuators (link ) - self-excite and synchronize without
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Museums and Inclusive Heritage Preservation Platform Labour, Creator Economies, and Algorithmic Change AI in the Creative Industries (cross-faculty potential) Independent Cinema Exhibition and UK Screen
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models optimised with evolutionary algorithms to address combinatorial optimisation in model design and the noisy nature of climate data. The Doctoral Researcher will receive on-the-job training in machine