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that can anticipate and adapt to the behaviour of humans and other autonomous agents. This PhD project aims to investigate the integration of Theory of Mind (ToM) reasoning into autonomous agents to improve
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mathematically in their full multiscale complexity, many complex systems leave warning signals in their data - subtle mathematical fingerprints that appear before a rapid transition unfolds. But reading those
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three coupled components. First, a physics-informed graph surrogate model will emulate network hydraulics at scale, representing pipes and assets as a graph and predicting flows, depths, surcharge
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geolocated social media data, and computational techniques from network science and machine learning. It is interdisciplinary, combining theories of healthy and accessible cities with computational data
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machine-learning-based surrogate models to accelerate design and control workflows. This PhD studentship would suit candidates with backgrounds or interests in engineering, physics, applied mathematics
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computational project where you will use a variety of methodologies to produce hierarchal 3D lattice structures, e.g. first principles calculations (density functional perturbation theory), molecular dynamics