49 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" positions at University of Sheffield
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. Into the second year, the project moves toward methodology refinement and Machine Learning integration. The student will execute a more ambitious cycle with a complex alloy system and integrate machine learning
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Automatization and Digital Enhancement of Characterisation Techniques: Joining the Dots between AI, Machine Learning and Materials Advances School of Chemical, Materials and Biological Engineering
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, the project proposes to also use machine learning techniques to learn parts of the prior and penalty structure from data in an interpretable way. Examples include mapping liquidity and volatility features to a
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with the CDT’s aim to achieve a sustainable wind farm lifecycle by developing methods for high-value reuse of composite turbine blades. Machine learning and non-destructive evaluation techniques will be
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very difficult. In other large scale machines (e.g. hydro-electric power stations, ships propeller bearing) sliding type or ‘hydrodynamic’ bearings [4] are much more common. There is increasing interest
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work with the UK semiconductor industry. The studentship represent a unique opportunity to be trained in the epitaxy process and to work in an emerging and exciting area of combining AI/machine learning
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-standard queries. Essential Application/interview Good understanding of computer operating systems (Windows and Mac OS) and desktop software (MS Office, web browsers) and an interest in learning new
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experience, or keen to learn the research knowledge in power systems, cyber-physical systems, computer science, information and communications technologies, and computing and data platforms. The perspective
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, water quality and meteorological datasets routinely collected by water utilities. The student will have the opportunity of using state-of-the-art machine learning methods (predictive analytics) to analyse
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hydro-climatic conditions govern vegetation behaviour, and how vegetation impairs the functioning of drainage and water-management assets. Using advanced geospatial modelling, machine learning and digital