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Artificial intelligence and machine learning methods for model discovery in the social sciences
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the prediction of chip formation and service life of the components. Previous experience / requirements: Experimental methods, Finite element modelling, computer programming, Manufacturing Please contact Dr
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The Development of Models for Mercury Oxidation in Oxyfuel Combustion School of Mechanical, Aerospace and Civil Engineering PhD Research Project Self Funded Prof Mohamed Pourkashanian, Prof Lin Ma
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accurate models are leading to very challenging computational questions related to the computational efficiency and effectiveness. To address these challenges, many computationally efficient modelling and
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to adapt to the modified geometry to model such phenomenon. The aim of this project is to utilise the Lattice-Boltzmann method, which does not require a computational grid to resolve the flow field in order
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Hughes, Prof Lin Ma, Dr Janos Szuhanszki Application Deadline: Applications accepted all year round Details Computational Fluid Dynamics modelling is a powerful tool that, due to recent advances in
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/ requirements: Experimental methods, Finite element modelling, computer programming, Manufacturing Please contact Dr Hassan Ghadbeigi (h.ghadbeigi@sheffield.ac.uk) for more details. Funding Notes This project
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to radar and other types. The challenges both from modelling, sensing, filtering and decision making point of view can be considered. Potential methods involve a broad range of machine learning methods
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complexity, classical control techniques cannot be easily applied because of computational bottlenecks or an absence of suitable prediction models. Distributed control approaches have been conceived to handle
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parameter selection and process optimisation. Previous experience / requirements: Experimental methods, Finite element modelling, computer programming, Manufacturing Please contact Dr Hassan Ghadbeigi