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strategic priorities in Digital Twins, Environmental Intelligence, and Data-Driven Engineering, using advanced computational modelling to support ecosystem resilience and sustainable management. The project’s
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methodologies, including experimental design and data-driven modelling How to apply Interested applicants should contact Carl Diver (c.diver@mmu.ac.uk ) for an informal discussion. To apply you will need
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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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adapting their energy use, control strategies, and collective behaviours to enhance sustainability. The research aims to design AI-driven control and energy management frameworks that enable self-organising
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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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affect plankton-driven processes and carbon cycling remains fragmented, with few quantitative datasets under realistic ecological conditions. This PhD project will bridge that gap by combining field