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Field
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be paid to the following experiences: -Experience in sampling and analyses of building materials -Experience in Life Cycle Analysis in construction sector. -Experience in building information modeling
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
this, you will work with emerging AI techniques such as large language models, which can interpret technical documentation, and knowledge graphs, which help structure and connect engineering knowledge
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duties, up to a maximum of 20 per cent of full-time. Your research focus will be on models and simulations of reactive gas flows, with applications in thin film depositions. Thin films form the basis
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gastruloids as a model system with which to study GAG structure/function relationships. Gastruloids are generated from induced pluripotent stem cells (iPSCs) and create in vitro models with which we can study
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. Development of numerical models for system descriptions. Lab-scale tests of a number of UV-AOPs (eg. VUV, UV/H2O2 and UV/O3) using a collimated beam approach. The effect of water composition on the AOP
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the organising principles of biological information processing, focusing on the underlying physics of computations and sensory environments. This exploration has led to developing models and advanced statistical
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capture technologies. In this project, you will: Develop a 3D Digital Model: Create an advanced computational model of high-pressure mechanical seals. Apply Computational Fluid Dynamics (CFD): Simulate gas
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Job Description If you are ready to launch your research career in advanced manufacturing and want to build cutting-edge skills in AI and real-time data-driven production, this PhD opportunity is
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, the research will focus on how ESG indicators can be meaningfully embedded into portfolio assessments and risk models. For instance, should a construction firm that actively reduces its environmental impact be
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PhD Studentship: Optimisation of Liquid Metal Filtration and Cleanliness in Nickel Based Superalloys
methods, and to develop tools for interpretation of model outputs. To apply models to discover filter structures / geometries for which the predicted filtration efficiency has been optimised. To undertake