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learning models to distinguish between normal physiological behaviour (e.g. diurnal rhythms, feeding responses) and abnormal stress-induced patterns will be central to the project. This requires
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scientific machine learning-based strategies for the discovery of self-similarity laws, use of quantised local reduced order models, and real data assimilation. You will be assimilated, jointly
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contribute to pioneering computational tools that extend far beyond potassium. About HetSys: Harnessing Data, Modelling and Simulation for Real‑World Impact HetSys (Centre for Doctoral Training in Modelling
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and AHWs, using realistic simulations; identify whether and how ecological interactions shift in response to heatwaves, using community-scale mesocosm experiments. The project will be based within
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, are crucial for modelling volcanic processes and are vital for understanding the transitions. Geophysical monitoring provides essential information to constrain these parameters and inform decision
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modelled using UK-based case studies, selected from a shortlist in Isle of Portland, S Wales, SW England, and the Peak District. The work will be supported by Deep Digital Cornwall at Camborne School
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of their impacts and how to quantify them. There have been attempts to estimate impacts based on empirical approaches (e.g. Bradshaw et al. 2022), while dynamic process-based models are also known
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and the British Geological Survey (BGS) will help ground the research in operational reality. 4. Adaptive Planning: Modelling insights will be translated into a scenario-based planning toolkit
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early as the advert may be removed before the deadline. High-dimensional computations are ubiquitous in science and engineering, often arising from models with numerous parameters. For instance
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aims to design and study advanced porous solids, such as MOFs, COFs, zeolites, carbonaceous materials, etc., for: Selective CO₂ capture and separation from gas streams Adsorption-based cooling and heat