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Experience in working with robotic systems for HDX workflows Excellent technical skills in sample preparation and HDX-MS data analysis Extensive and up-to-date theoretical and practical knowledge in working
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academic staff and industry professionals Further information on KTP, including case studies, can be found at; http://www.manchester.ac.uk/collaborate/business-engagement/knowledge-exchange
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leverage clinical and experimental datasets, apply physics-based simulations, and harness the power of scientific machine learning, including data assimilation and uncertainty quantification. Additionally
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the University joined the Alan Turing Institute, the UK national institute for AI and Data Science. As a University of Manchester staff member you will be fully supported in any application to become a Turing
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, consent, randomisation, guiding / supporting volunteers through the trial processes, data collection and analysis, consultation with other researchers, presentation/reporting and preparation of manuscripts
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experience acquiring 4-dimensional STEM (4DSTEM), differential phase contrast (DPC) and ptychography data being desirable. Direct experience using JEOL ARM300CF, FEI Talos F200A or FEI Titan “ChemiSTEM” (G2 80
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objectives, finalise study data and close down research sites. In collaboration with the research team, the post holder will be responsible for the planning and co-ordination of the day to day running of
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the key stages of an employee lifecycle Commercial Awareness - Understands the importance of dealing sensitively and confidentially with people data and personal information. Analytical Thinking - Sound
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information, please visit: https://www.bmh.manchester.ac.uk/about/equality/ Our University is positive about flexible working – you can find out more here Hybrid working arrangements may be considered. Please
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the groundwork for developing a digital twin (DT) for Greater Manchester’s Integrated Water Management Plan (GM-IWMP). The successful applicant will map and prioritise key water processes in GM, address data