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programming (e.g., Python, MATLAB). Energy system modelling expertise with experience in academic research Preferred Skills: Educational background in Electrical Engineering, Computer Science, Renewable Energy
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include a research proposal aligned with one or more of the following priority areas: Research Themes Multiphysics and data-driven constitutive modelling of traumatic brain injury and its long-term impact
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Start Date: Between 1 August 2026 and 1 July 2027 This project aims to frame hypersonic aerodynamics as a grand inverse problem. By combining modern state-of-the-art AI (foundation models, physics
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loads in power system dynamics and stability as system strength continues to decline. Building on existing frameworks such as the WECC Composite Load Model (CLM), you will develop and validate data-driven
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stability as system strength continues to decline. Building on existing frameworks such as the WECC Composite Load Model (CLM), you will develop and validate data-driven methods for load identification and
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. This PhD will design methods that enable robots to achieve more robust, accurate perception and perception-driven planning for complex processes. You will investigate solutions like multi-sensing fusion (e.g
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real-time rerouting recommendations. Beyond the PhD, the project’s data-driven models will evolve through continuous real-world updates, contributing to sustainable aviation practices and climate-aware
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PhD project: Modelling Reliability and Resilience of Hydrogen Systems for Improved Safety and Sustainability Supervised by: Rasa Remenyte-Prescott (Faculty of Engineering, Resilience Engineering
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loading conditions. By generating datasets from finite element simulations, ML models can learn the mapping between unit cell design parameters and homogenised properties. State-of-the-art approaches
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as early indicators of anthropogenic and climate-driven change. However, limited understanding of the processes shaping species’ biogeographic distributions constrains our ability to predict ecological