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under uncertainty (Studentship code MSP106) Learning to sample: Meta-optimisation of gradient flows using reinforcement learning (Studentship code MSP107) Dynamic Bayesian modelling of endurance sports
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receive training and skills in some of the following: meta-barcoding, stable isotope analysis, trophic-web analysis, Bayesian statistics, wet-lab experimentation – respirometry, fieldwork. Previous
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pathways are connected along extended depth gradients of 0-90 meters. Based in Newcastle, and with international supervision, you will receive training and skills in some of the following: meta-barcoding
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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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. Changes in water table pre- and post-restoration will be monitored using the existing network of piezometers distributed across the floodplain and additional piezometers installed. Additional measurements
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new brain stimulation methods, you will contribute to developing closed-loop algorithms for regulating brain dynamics with clinical applications in epilepsy and psychiatric disorders. Number of awards