697 maynooth-university-programmable-city-project positions at University of Sheffield
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(PIs), effectively support a diverse portfolio of research projects in line with University and funder guidelines and policies. Act as a key interface between PI and RPI, Faculty Research Hubs, Faculty
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Deadline: 21 August 2025 Details Project type: Academic-Led project Supervisors: Dr Ning Ma and Professor Jon Barker, University of Sheffield Project Partner: Yorkshire Ambulance Service NHS Trust Your PhD
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, Recruitment Co-ordinator on r.d.swift@sheffield.ac.uk Our vision and strategic plan We are the University of Sheffield. This is our vision: sheffield.ac.uk/vision (opens in new window). We are a Disability
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Hangsterfer's Laboratories) EPSRC Centre for Doctoral Training in Machining, Assembly, and Digital Engineering for Manufacturing PhD Research Project Directly Funded Students Worldwide Dr Thawhid Khan, Prof
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realising this ambition through the design, implementation and continuous improvement of our strategic planning framework. This work ensures the University remains clear, agile and decisive in achieving its
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Atomistic Simulations of Functional Ceramics for Power Electronic Applications School of Chemical, Materials and Biological Engineering PhD Research Project Directly Funded UK Students Dr C L
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Research/Industry Experience: Split your time between advanced laboratories at The University of Sheffield and John Crane’s Research Centre in Manchester and Manufacturing site in Slough. Project Overview
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take place on Wednesday 23 July 2025. This will consist of anInterview panel and Task. We plan to let candidates know if they have progressed to the selection stage on the week commencingMonday 7 July 2025
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the availability of codes and data developed throughout the project, adhering to the University's Open Research policy and FAIR principles Report results and progress in regular project’s meetings. Provide
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contribute to advancing simulation-based testing methods for ADS. You will contribute to cutting-edge research projects, including the EPSRC-funded SimpliFaiS: Simplification of Failure Scenarios for Machine