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addresses a core open problem in 6G and NTN design: how to maximise wave-based degrees of freedom (DoF) and channel capacity when links are influenced by near-field behaviour, environmental scattering, and
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Autonomous vehicles (AVs) are advancing rapidly, yet their safety assurance remains limited by the opacity of modern AI systems. Current machine learning–based decision pipelines often function as
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background in physics, biophysics, biological physics, or bioengineering. This PhD project will primarily focus on experimental research, which will include data analysis and there is scope for modelling
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to their presence during manufacture and storage. The effect of exposing a material to either of these species is likely to affect its onward behaviour, and data on these processes will support predictive modelling
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combining high-fidelity computational modelling with artificial intelligence to overcome key barriers in performance. The investigation will focus on optimising core gas exchange and combustion processes
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/TEM and XRD. In Physics all the requirement equipment for radiation-based characterisation of the materials is available, including X-ray luminescence and radioisotope gamma irradiation