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, and international studies—with cutting-edge data science techniques, including Earth Observation (EO) data analysis, machine learning, large-scale collation and analysis of survivor narratives
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, their empirical security assessment remains underdeveloped, representing a growing and critical gap. Recent studies have highlighted vulnerabilities in learning‑enabled CPS, demonstrating that small, carefully
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industry and academia together to drive pre-competitive, fundamental research in polymers. We welcome applicants with interests in polymer physics, materials processing and characterisation, machine learning
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moisture risks, directly affecting children’s health, wellbeing, and learning outcomes. This PhD will develop evidence-based, Passive House–informed retrofit strategies tailored to diverse school typologies
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into the sample of interest. Recently we have been using AI and machine learning to predict the distortion present and significantly speed up this correction process. This PhD project will take the latest in AI
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establish a digital route to quantify the segregation behaviour of residual elements at austenite/austenite grain boundaries through atomic-scale simulations, using modern machine learning techniques and in
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sensors - if we can control and tune their properties. You will develop and use top-of-the-line machine learning models to predict the sensor response of these materials under realistic conditions
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, machine learning, molecular dynamics, and fluid mechanics. We aim to understand how chemical structure of precursors and process conditions affect film quality, helping design better materials and