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primary supervision of Dr Thomas Ouldridge. The student will develop predictive models of nucleic acid strand displacement rates to allow the rational design of complex networks of ever-increasing
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of pharmaceutical formulations and molecular recognition processes relevant to drug delivery systems. The work focuses on molecular binding phenomena such as host–guest inclusion complexes and drug–excipient
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plasticity platform. Different machine learning strategies will be explored to capture the complex relationships between microstructural features and mechanical responses. In particular, the project will
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-making, and explainable reinforcement learning for large-scale public-health applications. You will: develop new multi-criteria reinforcement learning algorithms for complex decision problems: you design
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you often find yourself fascinated by the multiscale complexity of real-world engineering problems? In this project you will develop advanced multiphase transport models for porous systems, bridging
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emissions from diffuse and complex sources—such as energy infrastructure, landfills, and urban environments—remains a major scientific and regulatory challenge. This is particularly important in
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network integration for emerging low-energy opto-electronic AI systems and beyond. The challenge: Machine learning and neural networks are super-charging the complexity of problems that computer algorithms
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Mathematics, with a strong background in probability theory and complex analysis. Skills in differential geometry are also required. The thesis topics will focus on constructive field theory for conformal field
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plasticity platform. Different machine learning strategies will be explored to capture the complex relationships between microstructural features and mechanical responses. In particular, the project will
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to an interdisciplinary research environment at the interface of physics, epidemiology, and complex systems. This position offers the opportunity to work on a problem where theory, data, and societal relevance meet. The