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private. This PhD will focus on three strands of work: 1) Innovate NILM model structures. Design efficient neural network architectures for both aggregator and client models that meet strict accuracy
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. This probabilistic approach reflects the uncertainty and variability of geological systems, providing, in addition, a measure of confidence. (4) Calibration Ai architecture. The final component develops a calibration
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to guide conservation under accelerating climate change. This PhD project will therefore integrate climate data with physiological and behavioural systems, by creating a digital twin framework that
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costs [1]-[5]. Building on these previous findings, this PhD project will design a new MARL architecture that incorporates model uncertainty, cyber-attack scenarios, and network reconfiguration events