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) sensor data. This will be a small system-on-chip designed to operate on the edge (i.e. close to the sensor). The project will explore whether emerging logic-based ML algorithms can be translated
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to operate on the edge (i.e. close to the sensor). The project will explore whether emerging logic-based ML algorithms can be translated into smaller, faster, more energy efficient and cost-effective hardware
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hydrodynamic and water quality responses while providing robust uncertainty quantification to support reliable decision-making. Evolutionary algorithms will be employed to efficiently explore the parameter space
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reliable decision-making. Evolutionary algorithms will be employed to efficiently explore the parameter space and undertake sensitivity analyses. The integrated framework will be validated using analytical
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-weather perception for which Radar sensing/imaging is essential. This project focuses on developing algorithms, using signal processing/machine learning techniques, to realise all-weather perception in
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. An optimisation tool has been developed that uses a genetic algorithm to optimise the location of BGI taking surface water flood risk reduction and the cost of different interventions into consideration. This PhD