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Field
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uncrewed aerial vehicles (UAVs), within the context of national emergency logistics. The project will examine how these technologies can be integrated with existing infrastructure to support the timely and
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will examine and develop methods that go beyond the Markovian paradigm. It will consider a range of time series data, focusing on those that show challenging properties of uncertainty, irregularity and
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optical setup construction. They will make use of commercial simulation software to test electromagnetic designs, algorithmic coding to design metamaterials, fabrication techniques to produce
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optical setup construction. They will make use of commercial simulation software to test electromagnetic designs, algorithmic coding to design metamaterials, fabrication techniques to produce
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testing) to understand and tailor the physical and chemical interactions within these complex structures. Cranfield University is internationally renowned for its research into materials for extreme
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of the particle fuel, crack initiation/propagation and failure mechanisms in relation to test temperature. Finite element (FE) modelling using FE tools such as Abaqus, (or) Ansys, (or) COMSOL is optional
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including machine learning. This research will support the path to net zero flights and there will be opportunities to become involved in practical aspects of fuel system design and testing during their PhD
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, potentially including machine learning. This research will support the path to net zero flights and there will be opportunities to become involved in practical aspects of fuel system design and testing during
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markers. Develop machine learning models capable of predicting Category 1 emergencies based on real-time audio features extracted from calls. Work iteratively with YAS researchers to test and refine
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Academic score of 6.5 (with no less than 5.5 in each component test area) or equivalent. The studentship is supported for 3 years and includes full Home tuition fees plus a stipend of £19,215 per annum 2025