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granulation process. The aim of this project is to use Industry 4.0 technologies including machine learning and artificial intelligence (AI) to develop digital and soft sensors to predict product properties and
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close to completion of a PhD degree (or have equivalent experience) in a subject related to robotics, machine learning, and artificial intelligence. (assessed at: application) Integration of sensors and
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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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at the University of Bristol. We seek self-motivated, innovative and creative candidates with a PhD in a relevant field (or equivalent experience) along with significant scientific experience acquired through
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Physics informed learning for high fidelity medical simulators School of Electrical and Electronic Engineering PhD Research Project Self Funded Prof Sanja Dogramadzi Application Deadline
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capable of predicting boiling and CHF in PWR-relevant conditions. Combining improved physical modelling with the potential of machine learning and data assimilation techniques, you will specifically target
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and monitoring data can be used to develop machine agnostic process control methodologies. Perform metallographic and non-destructive assessment of LPBF builds to assess and classify build quality
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Modelling, DEM simulations and Machine Learning Approaches. You will have access to the excellent training opportunities at the University of Sheffield. A range of highly desirable skills will be learnt
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tasks and meet deadlines rigorously. [assessed at interview] Desirable criteria Familiarity with neo4j, python, Flask and software containers. [assessed at interview] Familiarity with machine learning and
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Machine Learning Methods for Autonomous Robot Navigation, Localisation and Pipe Inspection School of Electrical and Electronic Engineering PhD Research Project Self Funded Prof Lyudmila Mihaylova