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Field
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have partnered with AWE to fund a 4-year Computational Project to use state-of-the-art Computational Chemistry techniques to understand structure-property relationships in oxide scintillators. We will
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the menopausal microbiome using innovative experimental models, supported by structured training in preclinical models, batch fermentation, human colon models, LC–MS–based metabolomics, and next-generation
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with Graphs led by Prof. Nils M. Kriege. Our research focuses on the development of new methods and learning algorithms for structured data. Graphs and networks are ubiquitous in various domains from
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computational work involves protein structure prediction Publications in high impact factor journals Knowledge and experience in AMR, Artificial Intelligence in health and food systems Good laboratory skills
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jointly learn from images and text, most current systems are still limited in three important ways: they primarily rely on statistical pattern recognition rather than structured clinical reasoning
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stormwater) are a sustainable solution, but their long-term performance often suffers due to generic design approaches that overlook plant ecology and the interaction with soil structure development. This PhD
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approaches that overlook plant ecology and the interaction with soil structure development. This PhD project will transform rain garden design by linking plant traits to hydrological and ecological outcomes
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-driven AI models that capture the underlying process–structure–property relationships governing metal additive manufacturing. By combining mechanistic modelling, in-situ sensing, and machine learning
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an annual stipend of up to £10,000 Supervision and guidance from leading experts in the field Structured research training at SOAS. Access to workspace and resources to attend and present at national
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film flow within the microscopic seal gap. Couple CFD with Structural Models: Study the fluid-structure interaction (FSI) and dynamic response of seal rings under real-world conditions. Collaborate with