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on Artificial Intelligence (AI), Deep Reinforcement Learning (DRL), and Predictive Maintenance for optimizing wind turbine performance and reliability. This research will develop an AI-powered wind turbine
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model of the lower airways, multi-omic analyses will provide a key tool to characterise mechanistic changes and assist in predicting the human health impact of the exposure to different air pollutants
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Fully-funded PhD Studentship: Adaptive Mesh Refinement for More Efficient Predictions of Wall Boiling Bubble Dynamics This exciting opportunity is based within the Fluids and Thermal Engineering
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) approaches, along with Large Eddy Simulation, have demonstrated maturity in the prediction of many buoyancy-driven flows but require extensive validation. Two- and three-dimensional Computational Fluids
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of investigation, many predictive tools lack robust ways to incorporate uncertainties in boundary conditions, turbulence modelling, and manufacturing variability. Problem Statement Conventional CFD workflows assume
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Department: Materials Title: Predicting and Improving the Quality of Recycled Plastics Using Advanced Metrology and Data Science Application deadline: 29/04/2025 Research theme: "Materials
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PhD Studentship: Optimising Observations To Improve Early Warning Skill of Predicting Tipping Points
improving early warning skills of predicting tipping points with relevance to understanding potential future changes in the subpolar gyre of the North Atlantic. The integration of advanced climate models with
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Research theme: "NetZero", "Energy", "Decarbonisation", "AI in Engineering" How to apply:uom.link/pgr-apply-2425 No. of positions: 1 This is a fully-funded Strategic Doctoral Landscape Award (EPSRC-DLA) Scholarships from the School of Engineering. Funding to Home / UKRI level (£19,237 for...
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prediction of e.g. diffusion coefficients than is possible with existing techniques. In the first year, you will carry out an in-depth literature review on the methodology and materials to be studied, and you
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to the development of multiscale computational models for simulating crack propagation and establishing reliable methods to predict the residual strength of composite structures. The simulations, performed in Ansys