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Virtual Training Environment (VTE) for disaster response simulation, integration of Building Information Modelling (BIM) with Structural Health Monitoring (SHM) using smart sensor networks, and resilience
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Dr Sendy Phang. The student can gain experience and skills in a range of topics, such as Artificial Intelligence and Deep Learning, nanofabrication, computational modelling, metamaterial design, and
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experiments in controlled conditions aiming at simulating natural environments; 3) You will participate in arctic campaigns and use the methods developed to shed light on methane cycling in permafrost regions
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, structural equation modelling, visualisation, preferably in R Competences in quantitative research methods – ideally knowledge of several of the following aspects of quantitative data analysis: experimental
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Dr Sendy Phang. The student can gain experience and skills in a range of topics, such as Artificial Intelligence and Deep Learning, nanofabrication, computational modelling, metamaterial design, and
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signs of cardiovascular changes, adaptively model physiological patterns, and identify predictive biomarkers of maternal health. You will develop and apply cutting-edge techniques in: Signal processing
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real-time fault detection and predictive maintenance strategies. 3. Validation of AI models with real-world SCADA data, ensuring industry relevance. 4. A digital twin framework for safe, simulation-based
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vegetation results in different temperatures in the sunlit and shaded areas that depend on the solar radiation, physical state of the surface, and meteorological conditions. Models that combine the simulation
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and the effects of disordered correlated microstructures on diffusion; iii) development of energy-based models and numerical simulations of hyperuniform assemblies; iv) development and application
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, natural hazards management or related fields Interested in protective forests and their management Good quantitative skills (e.g., data analysis, simulation modelling, remote sensing) Good communication