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deep learning theory, Bayesian statistics, and generative modelling, this work will advance our understanding of both the capabilities and vulnerabilities of modern AI systems. This will have potential
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independently and collaboratively Excellent written and verbal communication skills Desirable Experience with modelling tools (e.g., MATSim, UrbanSim, ActivitySim, GIS, or Statistics) Experience in stakeholder
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statistical analysis and/or coding (e.g., R, Python, C++) Exposure to neurophysiological measurement methods (e.g., eye-tracking, pupillometry) Interest or training in technology law, digital regulation, or AI
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half years to complete. Ideal candidate This PhD research opportunity would suit someone with a strong quantitative background (e.g. econometrics, statistics), and an interest in applying their skills
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), singular optics, using electrons, atoms and light and the exploration of complex systems using statistical field theory. "Catastrophes on order-parameter manifolds" (with Dr Alexis Bishop and Dr Timothy
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models that can forecast the likely outcomes of current practices. The project aims to develop cutting-edge machine learning and statistical risk prediction techniques to predict each short-term, long-term