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Field
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Bayesian inference framework for identifying complex aerospace systems combining with limited experimental data. It can be also used to quantify uncertainties from experimental testing, significantly
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for platelet production, behave in the body using advanced imaging techniques. Using genetically modified mice that allow real-time tracking of cellular signalling, the project will explore how megakaryocytes
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): Network-level design and multi-node cooperation (coordination, topology design, distributed/federated learning, etc.) Wireless resource allocation and scheduling under multi-objective KPIs (rate, latency
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
engineers detect faults earlier, track system degradation, and make better-informed maintenance decisions. But how can we turn this complex information into something reliable, explainable, and actionable
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The primary objective of this project is to establish the evidence base on professional cycling road ‘racing’ trends and the critical tactical moments that determine how races are won. This evidence
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the direction of the Principal Investigator • To assist with the development of research objectives and proposals • To conduct individual and collaborative research projects under the direction
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in e-learning IT skills, esp. an object-oriented programming language such as C#, Python, or Java Knowledge in: logistics or/and supply chain management or/and transport planning is required Excellent
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practices (e.g., Buddhism, you will have a track record of interdisciplinary work at the nexus of artificial intelligence and neuroscience. Experience collecting multi-modal neurophysiological data would be
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environments—such as fleets with multiple aircraft types. Objectives Objective 1: Map current data types, structures, and interoperability challenges to build a detailed "as-is" understanding of current
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rated as ‘world-leading’ or ‘internationally excellent’. This PhD will be based within the Centre for Space Systems and the Resilient PNT research group, both of which have strong track records in