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modelling. Therefore, significant efforts are being devoted to understanding possible mechanisms and their effective modelling, with the aim of predicting transport/reaction characteristics of foam materials
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Department: Mathematics Title: Multiscale modelling of polymer network constitutive behaviour Application deadline: All year round Research theme: Applied Mathematics How to apply: Click the 'Apply
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developing better models for fragmentation of metals that include a consideration of the structure at the micro-scale, linking this to fragment formation at the macro-level. This will build on work in crystal
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recommend that you apply early as the advert may be removed before the deadline. This fully funded PhD position, offers an exciting opportunity to develop and optimize multiscale models for surfactant
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2026. We recommend that you apply early as the advert may be removed before the deadline. The modelling of laser-material interactions is a complex multi-physics problem, very computationally intensive
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to increase each year. We recommend that you apply early as the advert may be removed before the deadline. A modern area of research within probability theory is the study of a class of models named after
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. A modern area of research within probability theory is the study of a class of models named after Kardar, Parisi and Zhang and referred to as KPZ. This includes mathematical models for the random
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Application deadline: 27/03/2026 Research theme: Applied Mathematics, Thermal-Fluid-Dynamics, Multi-Physics UK only This 4 year PhD project is fully funded and home students. The successful candidate will receive an annual tax-free stipend set at the UKRI rate (£20,780 for 2025/26) and tuition...
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is addressed within the current generation of design codes through simplified analytical or empirical models. It is known that these can produce conservative outcomes. Improved prediction tools offer
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2026. We recommend that you apply early as the advert may be removed before the deadline. This PhD project will develop generative hybrid models that integrate AI foundation models with physical