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Field
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emissions and are being implemented to provide a carbon efficiency assessment of farm enterprises. The contribution and management of ensiling at the farm scale to these models has not been fully explored
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health management (IVHM) system that leads to enhance safety, reliability, maintainability and readiness. Generally, prognostics models can be broadly categorised into experience-based models, data-driven
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including predictive modelling, computer vision and epidemiology. The student will join an established team of investigators, including statisticians, epidemiologists, image scientists, and clinicians
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fine-scale, fully distributed hydrological modelling, with the ultimate goal of optimising NFM strategies in moorland, to improve flood resilience for rural, upland communities. The studentship will be
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will contribute to the field by: Developing a conversational AI interviewer capable of conducting real-time adaptive interviews. Building an automated candidate ranking model based on interview
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process conditions. Furthermore, this research will focus on the development of a model, allowing for virtual testing and optimisation of the chemical recycling process. This includes potential
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for household who stay indoors, and to prepare for emergency responses. Possible quantitative methodologies include concurrent time-series analysis of outdoor and indoor environment data, prediction model
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modelling to provide a robust framework for integrating nature-based solutions into SO management. This can alleviate the pressure on treatment infrastructure and reduce dependence on grey infrastructure
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from the Newcastle Urban Observatory, public transport ridership data, questionnaires and travel surveys, and the DARe MATSim agent-based modelling suite, this PhD will seek to gain a deeper
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identifying a novel biomarker for early decline in the heart-brain-axis, this study aims to explore the effect of alpha-synucleinopathy in heart and stellate ganglia. Using a PD animal model with a neuronal