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/learning based techniques in the areas of robotics, or autonomous systems, • interested in autonomous systems and signal processing, • Keen to work with equipment and embedded
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project will develop novel methods for modelling and controlling large space structures (LSSs), so that they can be reliably utilised in space-based solar power (SBSP) applications. Working with leading
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-efficient research that prevents fatigue failures has pushed towards integrated computational materials engineering approaches that improve competitiveness. These approaches rely on physics-based models
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/learning based techniques in the areas of robotics, or autonomous systems, • interested in autonomous systems and signal processing, • Keen to work with equipment and embedded
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critical to ensuring the longevity and safety of fusion reactors. This PhD project focuses on developing an integrated framework that combines cutting-edge computational models, including Monte Carlo
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intelligence, multi-agent systems, and the design of AI models. They will also acquire transferable skills in interdisciplinary problem-solving and innovation, which will significantly enhance
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a comprehensive, multi-fidelity suite of liquid hydrogen (LH2) pump models to predict and analyze pump performance, stability, and its interaction with the broader fuel system architecture for a
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This self-funded PhD research project aims to develop smart sensors based on low-frequency resonance accelerometers for condition monitoring of ultra-speed bearings. The developed smart sensors will
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zone in a very complex manner and lead the modelling to an imperfect zone of assumptions. These complexities allow the researchers to use approximations for useful lifetime calculations. Based
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the intersection of ecology, machine learning, and sustainable land management, the research will combine field data collection, deep learning model development, and stakeholder co-design to support biodiversity