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computational methods for modelling social dilemmas that can account for real-world complexity in agents’ behaviour. We will build on novel computational techniques to produce realistic enough models that can be
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would develop new computational methods for modelling Groupthink that can account for real-world complexity in agents’ behaviour, and build realistic enough models that can fit past and present empirical
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systems and swarm robotics. The project builds on well established computational and mathematical modelling techniques to achieve its aims. Departure points will be agent-based simulations, optimisation
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of Machine Learning (ML) models across large-scale distributed systems. Leveraging advanced AI and distributed computing strategies, this project focuses on deploying ML models on real-world distributed
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cutting-edge AI methodologies, focusing on combining data-driven approaches with physics-informed models to tackle challenges in MRI reconstruction. By integrating MRI acquisition physics directly into
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models for deployment in real-world driving scenarios. Required knowledge First-class bachelor’s honours or master’s degree in computer science, engineering, or a related field. Alternatively, second upper
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. A systems mindset and the ability to navigate complex, multi-stakeholder environments. Strong leadership in multidisciplinary teams, especially at the intersection of modelling and policy. Exceptional
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physicist with an outstanding research record in one or more areas of theoretical quantum science, including: Quantum computing Quantum information Quantum communication Quantum sensing Quantum optics Quantum
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diabetes management system using a mobile app to rate foods based on the glycaemic response of an individual. AI models will be trained on both the food intake and blood glucose data, and learn from
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Dowe, 1999a) ensures that - at least in principle, given enough search time - MML can infer any underlying computable model in a data-set. A consequence of this is that we can (e.g.) put latent factor