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-informed machine learning (PIML) with domain-specific engineering knowledge. By embedding physical laws and corrosion mechanisms into data-driven models, the research will produce more accurate
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Marine Laboratory. The partnership aims to provide a broad training in earth and environmental sciences, designed to train tomorrow’s leaders in earth and environmental science. For further details about
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honours degree or a Masters qualification in ecology, marine biology, data science, environmental sciences, or related fields. Candidates with interdisciplinary backgrounds and strong quantitative skills
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of the candidate Essential requirements: A 1st class or 2.1 degree (or equivalent) in Environmental Science, Remote Sensing, Computer Science, Surveying Engineering, or related field Strong coding skills (Python
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) Related undergraduate / masters degree (chemical engineering, chemistry, physics, environmental chemistry / engineering, or similar) Willingness to learn and adapt to new disciplinary areas. Ability
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ambient energy and transmit data wirelessly, minimising human intervention and enabling monitoring in remote terrains. SPS nodes can measure key environmental parameters such as temperature, humidity, soil
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studentship is closely related to the current cohort of MOAP-PhD students and the wider PhD cohort in the group of meteorology and climate and the School of Geography, Earth, and Environmental Sciences
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Geological Survey, Centre for Ecology and Hydrology, the Natural History Museum and Plymouth Marine Laboratory. The partnership aims to provide a broad training in earth and environmental sciences, designed
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intelligence (AI), which integrates diverse information sources including tabular, imagery, linguistic and acoustic data, has shown transformative potential in domains such as healthcare, environmental
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Geological Survey, Centre for Ecology and Hydrology, the Natural History Museum and Plymouth Marine Laboratory. The partnership aims to provide a broad training in earth and environmental sciences, designed