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offers the opportunity to contribute to an emerging area of interdisciplinary research that brings together artificial intelligence and advanced materials modelling to address key challenges in
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. Nonlinear finite element modelling and characterisation Conduct numerical modelling via nonlinear finite element analysis and physics-informed neural networks Materials characterisation for soft, textile and
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characterisation, to generate data-rich descriptions of evolving materials and processing pathways. A central aim is to couple these experiments with machine learning, mechanistic modelling, and automated data
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The goal of my research is to synthesise and characterise functional low-dimensional materials with atomic-scale precision and tailored electronic, optoelectronic, magnetic and chemical properties
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Risk-Relevant Biomarkers of Release Events) and one each for risk assessment (PhD-3 Quantitative Risk Assessment of Biofilm-Derived Hazards) and management (PhD-4 Predictive Modelling and Mitigation
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five‑year collaboration integrating materials growth, advanced microscopy and spectroscopy, nanoelectronic device fabrication, and machine‑learning‑accelerated modelling. As a member of my group, you
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applied physics other related disciplines. Demonstrated knowledge in at least one of the following areas: porous media flow computational fluid dynamics (CFD) pore-network modelling lattice Boltzmann method
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CAD modelling CFD Demonstrated ability to work independently and to formulate and tackle research problems. Demonstrated academic research experience as evidenced by publications in high quality
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organisations, the Institute delivers practical solutions and strategic insights across technology, policy, markets, and societal impact. From advanced energy materials and emerging technologies to shaping future
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that are found readily available in the food supply. In animal models, emulsifiers cause inflammation in the gut, similar to that seen in Crohn’s disease. This project aims at investigating if removing emulsifiers