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Area Engineering Location UK Other Digital-Twin Technology to Accelerate Development of Electric Propulsion Systems This exciting opportunity is based within the Power Electronics, Machine and
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of the covariance of physical laws under transformations between quantum reference frames. This is part of your personality: You have completed your Master's degree or Diploma in physics. You have experience in
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understood. Recent advances in omics technologies—genomics, transcriptomics, proteomics, metabolomics, and epigenomics—offer a powerful, integrative approach to comprehensively dissect the molecular landscape
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace - In Partnership with Rolls-Royce PhD
system health monitoring, and more efficient maintenance planning. Digital twins offer a powerful foundation but must evolve beyond simulation to truly support engineering decisions. This PhD will develop
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, Aerospace and Civil Engineering at the University of Sheffield, and embark on a transformative PhD project funded by John Crane Ltd, a global leader in engineering technology. What’s the Project About
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, stress markers). Ability to conduct quantitative and qualitative research, with experience in data collection, analysis, and interpretation. Willingness to work across disciplines, integrating health
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Net Zero 2050 goals, electric motors must undergo a transformational leap—from today’s typical power densities of 2–5 kW/kg to a step-change 10–25 kW/kg by 2035. The highest power dense motors today
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-mounted ground-penetrating radar (GPR) with smart meter data to transform leak detection. Drones equipped with GPR will identify underground anomalies, while smart meter data will refine localisation
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accelerate the world’s transition to carbon-neutral energy systems? Join the Thermofluids Group in the Department of Mechanical Engineering at the University of Sheffield, and embark on a transformative PhD
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
graphs, and ontologies, the research will help engineers transform unstructured maintenance records into explainable insights. By capturing both technical and experience-based (tacit) knowledge, the system