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, making use of intelligent AI-driven control planes. The applicants should have a solid theoretical background on machine learning, optical networks and fibre sensing and be willing to engage in testbed
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causal machine learning, transport behaviour analysis, and residential energy demand modelling to support sustainable urban and energy policy. The researcher will contribute to the design, implementation
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combining causal machine learning, transport behaviour analysis, and residential energy demand modelling to support sustainable urban and energy policy. The researcher will contribute to the design
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econometric and machine learning frameworks to analyse the causal relationships between transport behaviour, residential energy consumption, and environmental outcomes in urban systems. The successful candidate
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position is available to work in the area of Digital Twins for optical networks, making use of intelligent AI-driven control planes. The applicants should have a solid theoretical background on machine
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Geography, Botany and Zoology) and as part of the E3 learning foundry which includes the School of Natural Sciences, the School of Engineering and the School of Computer Sciences and Statistics. Even for