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emerging types of national emergencies and evaluate their spatial and operational implications. This will include an analysis of UK population distributions, terrain, infrastructure access, and airspace
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treatment, material and energy flow analysis, integrated data modelling, systems dynamics modelling, circular economy, sustainability assessment performance, decision-support tool design Month when Interviews
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untapped potential remains in extracting value from this data. This PhD will explore advanced analytics techniques, including machine learning, digital twin modelling, time series analysis, spectral analysis
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within a state-of-the-art cleanroom, experimental optical systems and data analysis. This research is ideally suited to candidates with interests in photonics, metamaterials, ultrafast optics
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objects, by embedding them into a 2 or 3-dimensional space through a representation learning algorithm, has been widely used for data exploratory analysis. It is particularly popular in areas such as
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approach including empirical data analysis, experiments, and theoretical modelling to develop science-based management strategies for the restoration of woodland ecosystems. We will collect, and collate from
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experience in developing computational models and implementing models for computer simulations. Software development in C++ and/or Python is expected, and experience in model analysis and parameter
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schedule of questions for research interviews. Conducting a series of semi-structured interviews at UoN. Transcription and analysis of qualitative data. Attending research team meetings. Contributing
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simulations and finite element analysis, with high-heat flux electron beam experiments. The research will simulate and replicate steady, cyclic, and transient thermal loads to better understand PFM behaviour
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sources compared with gas turbines, etc. The aim of this PhD research is to develop novel performance simulation capabilities to support the analysis and optimization for sCO2 power generation systems