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first-class or upper second-class honours degree in mechanical engineering or a related subject. This studentship will attract a stipend of £20,480 for four years. The position arises from an engineering
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The project: As wearable technology becomes increasingly ubiquitous in our lives, it is urgent we better understand how we might use the technology and how the technology can enhance our lives
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the recording of substrate-borne vibrations produced by earthworms (and other invertebrates) in situ. To fully exploit this emerging technology there is a need to unravel the meaning within the data. As such
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to an imperfect combustion in engines) and those emitted outside the exhaust (linked to the abrasion of tyres and the wear of brakes). The dynamics of exhaust and non-exhaust pollutants released into the atmosphere
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. The project is co-sponsored by Spirent Communications, a world leader in navigation and testing technology. Spirent will provide advanced simulation tools, expert support, and industry placements to help make
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your suitability with evidence of the following: Have backgrounds in computer science (or engineering), system engineering, or physics/mathematics. Knowledgeable in machine learning techniques (had
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this testbed available to users for testing hardware and applications. NPL will lead on the testing and security evaluation of the testbed and collaborate widely on the technology development. The student’s
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together leading research groups in the Department of Chemistry, Warwick Manufacturing Group (WMG) and the School of Engineering at the University of Warwick. The centre collectively has world-leading
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-on experience with real-world SCADA data, industry collaboration with RES Group, and training in high-fidelity simulation environments (OpenFAST, Digital Twin technology). This opportunity is ideal for those
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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