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such as Alzheimer's - but predicting how proteins fold in biological environments remains a key unmet challenge. This project brings together insights from efficient graph-driven folding simulations with
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barriers: a large input modality gap, as network data consists of diverse, non-textual formats like time-series metrics, graphs, and scalar values; the inefficiency and unreliability of answer generation
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value. Yet these trade-offs remain poorly quantified in complex urban landscapes. This PhD will investigate how urban blue networks can be optimised for both ecological resilience and community wellbeing
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the connections between clouds and climate. Ultimately, we want to create to causal graphs for large-scale cloudiness, its dependence, and its effect on the related environmental factors. Additional or alternative
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This PhD studentship covers fees and stipend for a home (UK) student to investigate how urban blue networks can be optimised to enhance ecological resilience and community wellbeing. The project
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PhD Studentship in Aeronautics: Real-time machine learning and optimisation for extreme weather (AE0073) Start Date: Between 1 August 2026 and 1 July 2027 Introduction: Climate change is
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(e.g., wind-turbine blades, rails, laminates). Building on our recent “FNO-Kernel” work—embedding a physics-based convolutional kernel inside the Fourier operator—the PhD will deliver operator-learning
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A PhD position is available at the Theory and Foundations group in the Department of Computer Science, University of Warwick, UK. The group works on various aspects of theoretical computer science
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graph, and discrete random processes. The aim of this project is for the student to develop an understanding of these tools and to apply these techniques to open research problems in the field. Entry
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multimodal data, ultimately uniting rigorous machine learning foundations with biological discovery. Project details This PhD project will contribute to the development of generative models for multimodal data