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—remains a critical challenge. This project will focus on designing AI-driven cognitive navigation solutions that can adaptively fuse multiple sensor sources under uncertainty, enabling safe and efficient
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, ensuring stable operation even as system dynamics evolve. Recent advances in Modular Multilevel Converter (MMC) topologies, along with developments in battery and supercapacitor technologies, create new
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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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the detection of real versus AI videos. The focus has been from single disciplines (e.g., Masood et al., 2022), and typically technical in nature. By drawing on multiple modalities within video (i.e., visual
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metrics during both standard operation (primarily governed by system reliability) and extreme events (primarily governed by robustness and restoration). This will be achieved by building on previous
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on the phase shift of vibration of the structure. However, the coupling effect of flow performance and vibration of structure, as the underlying mechanism of CMF operation, is not considered in the CMF
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—remains a critical challenge. This project will focus on designing AI-driven cognitive navigation solutions that can adaptively fuse multiple sensor sources under uncertainty, enabling safe and efficient
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realistic degradation from corrosion processes. The simulations will be integrated with mesoscale experimental to evaluate the constitutive response of smooth specimens degraded by corrosion. Given
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relevant to multiple applications, including small aircraft, drones, turbines, and other systems reliant on efficient fluid flow around foils. The project offers a unique opportunity to gain experience in
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Multiple self-funded PhD positions are available in Modelling and Simulation (M&S). The project will aim to mature software repositories describing the biomechanics of the human brain. The M&S tools