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and compare discrete “events” of coordinated movement and use our recently developed “swaRmverse” analysis pipeline [2] to perform inter- and intra-species comparisons of collective movement [3]. Based
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. The start date is October 2025. Are you passionate about applying computational science to real-world engineering problems? Do you want to develop digital twins of materials that can predict performance and
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an opportunity to network and get feedback on their work. Student profile: Some experience in data analysis would be very helpful, as would a working familiarity with a programming language (e.g. Python, R
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role: This post, funded by an ERC Consolidator Grant (DISCO), focuses on the dynamic behaviour of ferroelectric domain walls using advanced cryogenic and electrically biased 4D scanning transmission
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environmental impacts of digital activities. You will lead projects modelling the energy usage of different computing equipment (personal computers, servers, High-Performance Computing infrastructure
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on Artificial Intelligence (AI), Deep Reinforcement Learning (DRL), and Predictive Maintenance for optimizing wind turbine performance and reliability. This research will develop an AI-powered wind turbine
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will help characterise and develop optical techniques for satellite tracking, satellite identification and orbit determination. They will work with their supervisors and colleagues to further our
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The role will develop new AI methods for identifying the instantaneous state of a fluid flow from partial sensor information. The research will couple techniques from optimization and control theory
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the field of Computational Morphodynamics in plants. The work will be within the ERC-funded project RESYDE (https://resydeproject.org ) with the aim of building a virtual flower using multi-level data and
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fields, and risk damaging the part during fabrication. Finite element analysis (FEA) models, while capable of delivering detailed spatiotemporal distributions of thermal variables, suffer from limited