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Field
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within livestock environments contains a wealth of information about animal behaviour, health and emotional state, yet it remains relatively under-utilised in commercial dairy systems. This PhD will
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efficient interpretation of LiDAR data. The project will make use of high-performance computing facilities to support large-scale simulations and ensemble-based uncertainty quantification. A key innovation of
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data acquisition, or lithium-ion battery degradation analysis. Familiarity with EV, energy storage systems, or smart energy technologies. Experience working with large datasets, data-driven modelling
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solar installations, generating new understanding to underpin future large scale deployment in environments such as the Celtic Sea. The successful applicant will work at the forefront of marine renewable
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ecology techniques (e.g. radio-tracking, animal handling, behavioural observation), geophysical and surveying techniques (e.g. ground-penetrating radar, relative GPS), working with big data, statistical
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techniques employed include high-throughput cell based screening, flow cytometry and preclinical treatment models. Informatic approaches include data mining and manipulation of large pharmacogenomic matrices
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regulatory obligations. Hydraulic simulators are physically detailed but computationally slow and calibration-intensive, limiting large-scale scenario exploration and optimisation. Purely data-driven
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across disciplines An independent, self-motivated approach to research You'll gain skills spanning dynamical systems theory, data science, earthquake science, and soil ecology, working with large, real
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About the Project As federated learning systems become increasingly embedded in high‑stakes domains where data cannot be directly shared, such as healthcare and finance, the ability to selectively
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data such as online customer reviews. This work will produce a generalisable, open-source tool for computing variable-threshold accessibility indices. Examine the relationships between N and health and