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the spatial distribution of woodburning emissions. Integrate observations into inversion modelling to refine regional and national emission inventories. Model the impact of woodburning on UK air quality and
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networks perform well across UK towns and cities, where benefits are unevenly distributed, and how design or management interventions could enhance resilience and equity. A key component of the research will
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, integrity-aware multi-domain navigation benchmark and associated algorithms, tested in realistic operational environments. The outputs will support standardisation efforts, accelerate cross-domain navigation
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can feed directly into precision surgery algorithms and clinical trials. Few PhD projects offer such a clear line of sight from variant to mechanism to clinical translation. Located on the thriving
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different VME types. Build predictive spatial models for VME distribution and resilience under future climate scenarios. Training The candidate will gain skills in: Deep-sea survey techniques using remotely
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
Thermography. This raw dataset is needed to be processed and annotated to train supervised and unsupervised AI models. The research will aim to develop deep learning algorithms for damage classification
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-to-design mapping, mixture density networks or MDN-based inverse generators will be employed to capture the multimodal distribution of the design space, enabling flexible inverse design sampling based
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distribution and with synthesised future sea-ice distributions; examine the impacts on barrier wind structure and associated surface turbulent fluxes. Examine the frequency, characteristics and ocean mixed-layer
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physics all the way to numerical simulation algorithms? Then apply now to join our team of researchers in the Quantum Information and Quantum Many-Body Physics research group. Your personal sphere
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sustainability. The research will delve into power-aware computing strategies, thermal management, and the development of algorithms that balance performance with energy consumption. Students will aim to create