12 condition-monitoring-machine-learning PhD positions at University of Sheffield in Uk
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species, and the emergence of previously unseen classes. Recent advances in remote sensing and machine learning provide new opportunities to address these challenges, but most current approaches
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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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PhD: Systematic Exploration of Robot Behaviours for Manufacturing Tasks to Automatically Discover Failure Scenarios EPSRC Centre for Doctoral Training in Machining, Assembly, and Digital Engineering
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markers. Develop machine learning models capable of predicting Category 1 emergencies based on real-time audio features extracted from calls. Work iteratively with YAS researchers to test and refine
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. The experience can be in any health condition or health service. The role is based in the Health and Care Research Unit in the School of Medicine and Population Health at the University of Sheffield. In this unit
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project’s focus is to: Conduct cutting-edge experiments to investigate how surface texture affects seal performance and explore the use of an ultrasonic sensor for real-time monitoring. Experiment with
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interaction (FSI) and dynamic response of seal rings under real-world conditions. Collaborate with the Leonardo Centre for Tribology: Work with top researchers on experimental and modelling techniques. Why
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and heat transfer in geothermal systems under high-pressure and high-temperature conditions relevant to AGS. • Developing high-fidelity direct numerical simulation (DNS) models to map
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film flow within the microscopic seal gap. Couple CFD with Structural Models: Study the fluid-structure interaction (FSI) and dynamic response of seal rings under real-world conditions. Collaborate with
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performance and explore the use of an ultrasonic sensor for real-time monitoring. Experiment with ultrasonic sensors for real-time seal gap measurement. Combine experimental research and mathematical modelling