203 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:" positions at University of Sheffield
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for attaining desired engineering properties. The project will combine physical modelling, experimental data, and machine learning to create a feedback loop that refines the material properties and manufacturing
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the key data streams and advanced analytics methods (e.g., Machine Learning) required for a practical, production-ready system. Use signal responses to optimise process parameters, tool selection, and even
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Overview Campaigns and Alumni Relations (CAR) at the University of Sheffield is dedicated to inspiring alumni (former students) and supporters to make donations and volunteer their time. A gift to
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? This PhD project offers a unique opportunity to apply machine learning to solve a critical engineering challenge within the railway industry. The Challenge: Rail grinding is a crucial maintenance activity
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analytics methods (e.g., Machine Learning) required for a practical, production-ready system. Use signal responses to optimise process parameters, tool selection, and even rapidly qualify new tooling designs
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geometries typical of fusion reactor cooling systems. Compile a comprehensive dataset of boiling parameters to support machine learning-based analysis of two-phase flow behaviour. The successful applicant will
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2050. To decarbonise our airways and enable zero emission flights, we need high power-density, efficient and fault tolerant electrical machines. Cryogenic motors can provide the answer, however, this
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(CHF), tailored to complex geometries typical of fusion reactor cooling systems. Compile a comprehensive dataset of boiling parameters to support machine learning-based analysis of two-phase flow
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Using Brain Computer Interface to Improve Cognitive Performance School of Electrical and Electronic Engineering PhD Research Project Self Funded Dr Mahnaz Arvaneh Application Deadline: Applications
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Flexible or Flawed? A Computational Neuroscience Approach to Learning Strategies and Psychiatric Traits