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) of high-value critical assets. Through this PhD research, algorithms and tools will be further improved and developed, validated and tested. It is expected that combining the domain knowledge and the
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algorithms are used that allow a computer to process large data-sets and learn patterns and behaviours, thus allowing them to respond when the same patterns are seen in new data. This include 'supervised
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and adaptation Advanced sensors for water quality, wastewater-based epidemiology and point-of-care diagnosis Environmental biotechnology Environmental pollution, water-soil-waste system modelling and
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from such machines to derive algorithms expressing their state of health and next maintenance needs. A background in both engineering and machine learning would be useful, although help is readily
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) modelling, the project will optimise sensor placement and sensitivity. It will also evaluate how strain, temperature, and environmental factors influence hydrogen behaviour. Ultimately, this research seeks
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gas turbine sensor data, if available, will be utilized to validate the developed digital twin in order to estimate non-measurable health parameters of major gas path components, including compressors
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this issue and we could use obtain data-driven models using machine learning algorithms such as artificial neural networks, reinforcement learning, and deep learning. A typical caveat of data-driven modelling