667 data-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "Univ" positions at University of Sheffield
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be answered using primary mixed-methods data collection to offer a detailed, nuanced exploration of the research questions from diverse interdisciplinary perspectives. The project will include
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computing facilities, expertise in computer-aided engineering (CA2M lab), the available experimental facilities including mechanical testing and links with industry and with our Advanced Manufacturing
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coordinating contributions from partners. Information, Data and Governance Maintain secure filing systems for documents, ethics submissions, approvals and partner correspondence. Support adherence to GDPR, NIHR
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engagement and relationship management. Communicate information across different channels and audiences. Develop a plan for alumni engagement with MBA students identifying their potential contribution
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experiments and data analysis. Analyse and accurately record data to aid the design of future research and produce useful findings for other academics, funders, policy makers and practitioners. Plan and prepare
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data collection in specific locations (where relevant), and then analyse and interpret data generated by the project. Work in collaboration with the other project team members to develop novel
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University Executive Board. You will oversee governance, legal services, and data protection, and serve as the University’s Senior Information Risk Officer. This role is critical to the delivery of our One
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Assistant will maintain frequent contact with the PIs, liaising with them to ensure efficient and rigorous collection of field data. Applicants must have a BSc or MSc (or equivalent experience) in a relevant
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neutrino physics—using a combination of CMB and large-scale structure data. The analysis will rely on modified Boltzmann codes (e.g. CAMB or CLASS) and Monte Carlo techniques for parameter inference
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-CFD methodology by: • Developing advanced models for phase momentum and energy transfer. • Validating predictions against high-quality experimental data (e.g., void fraction and pressure drop