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the computational inefficiencies of physics-based models and enabling faster, potentially more accurate predictions. However, AI models require substantial volumes of high-quality, labelled training data, which
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intelligent systems aim to optimize power usage without compromising performance, employing strategies like power-aware computing and thermal-aware optimization. These systems are crucial in extending
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profoundly affect their mechanical properties and overall performance. Therefore, understanding the temperature field and developing effective thermal control techniques are vital to ensuring a high-quality WA
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on multimodal human trust estimation, trust-adaptive decision-making, or cognitive human–machine interfaces that enhance safety and performance in complex environments. This project offers a unique opportunity to
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. The studentship is funded by the Leverhulme Trust through the Connected Waters Leverhulme Doctoral Programme. Urban blue networks, including rivers, canals and wetlands, are dynamic systems that shape how cities
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Application Deadline 8 Mar 2026 - 23:59 (Europe/London) Country United Kingdom Type of Contract Temporary Job Status Full-time Hours Per Week 37 Is the job funded through the EU Research Framework Programme
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operation of autonomous systems in complex, real-world conditions. This PhD project aims to develop resilient Position, Navigation and Timing (PNT) systems for autonomous transport, addressing a critical
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institutions. State-of-the-art facilities: Access advanced laboratories and high-performance resources. Flexible learning: Tailored research projects aligned to personal interests and career aspirations. Career
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within the Centre encompass a very wide range of topics, including gas turbine performance modelling, propulsion aerodynamics, icing, combustion, computational fluid dynamics, advanced instrumentation
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to desalination and water reuse applications, for the selective separation of unwanted contaminants to produce high quality water. However, the polymer chemistry of the current class of reverse osmosis membranes