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for research into thermal management and system health monitoring, supporting studies in military aircraft systems. Engaging with these facilities allows students to acquire practical skills and technical
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biodiversity support, cooling, air quality regulation and access to nature. By integrating Earth observation, spatial AI, machine learning and socio-environmental datasets, the project will reveal where blue
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students that conduct research with academic leaders across leading UK institutions. Engage in online and face-to-face activities, including cohort-building events and collaborative learning exercises
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structure would enable you to understand science better at atomic level. You will learn the skills of presenting the results to small and large groups of people via presentations in conferences and meetings
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failures before they occur, enabling proactive maintenance strategies. Anomaly Detection Mechanisms: Implement machine learning techniques to identify and classify anomalies in electronic systems, enhancing
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and kinematic models with machine-learning-based channel state information (CSI) prediction to enable robust, low-latency connectivity across multi-layer NTN systems. This PhD project sits
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regulation and access to nature. By integrating Earth observation, spatial AI, machine learning and socio-environmental datasets, the project will reveal where blue networks perform well across UK towns and
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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
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aircraft, utilized for research into thermal management and system health monitoring, supporting studies in military aircraft systems. Engaging with these facilities allows students to acquire practical
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. Assess ecological change by applying shotgun metagenomics and amplicon sequencing to track microbial community shifts under persistent wet skimming. Translate lessons learned into engineering design rules