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Artificial intelligence and machine learning methods for model discovery in the social sciences School of Electrical and Electronic Engineering PhD Research Project Self Funded Prof Robin Purshouse
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of parallel computing (GPUs) to speed solution within the optimisation process. Funding Notes 1st or 2:1 degree in Engineering, Materials Science, Physics, Chemistry, Applied Mathematics, or other Relevant
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of numerical methods for solving scattering problems and inverse problems (assessed at: application and interview) Proficiency in scientific programming languages, Julia or Python (assessed at: application and
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research programme is to use genetic, molecular and cellular, and imaging/image analysis approaches to understand the development and function of the vertebrate inner ear, the organ of hearing and balance
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round Details The project is aimed at taking the advantage of the enormous potentials of the fast developing computing technology and numerical methods to develop new thermal hydraulics solutions and
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performance of the materials. A modelling platform will be developed in this project where the effect of various compositions of additives on CFRP properties will be simulated using numerical methods
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to the additive manufacture of an aluminium alloy. Develop melt pool physics simulations of the additive manufacturing process for the industry-based aluminium alloy. Develop computational methods for quantifying
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flares using the assumption of hydrogen recombination continuum emission, in place of a blackbody estimate. In parallel, but not in collaboration, Pietrow, Cretegnier, Druett et al. (2024) have advanced
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explore data-driven methods including machine learning (ML) and artificial intelligence (AI) techniques, to develop predictive HMPM tools that can diagnose, detect, and predict faults in machinery
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these methods for thin coated membranes. As part of a team, you will develop mathematical and computational models, as well as lead the experimental work. You will work closely with our industrial partner. You