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populations and space, particularly for vulnerable and disadvantaged populations. The RA will lead literature review searches to identify the historical, structural, and contextual drivers of disease risk and
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participants and engage with supporters, patients, and the public in workshops led by participants. These workshops provide opportunities to share learning experiences and offer feedback on the structure and
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well as its Information and Support Centre, to ensure the administrative systems and structure support the multi-strand activities of the Institute. You will also provide research support across NMPC, liaising
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benchmarking for specialty training. Teaching qualification or HEA Fellowship, and experience delivering structured teaching (UG/PG). Research experience—publications, presentations, or project work; knowledge
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assessments to identify how changes from the "as-is" processes, systems, and structures to the "to-be" processes, systems and structures impact specific services, processes, and stakeholders. Stakeholder
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pioneering work in infant brain imaging and for linking variations in brain structure and function to social and cognitive development. By promoting collaboration among academics, researchers, and clinicians
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(neuro)imaging datasets. Basic understanding of radiology clinical workflows and radiology report structure. The ability to take individual responsibility for planning and undertaking own work, according
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and support project teams and workstream structures to deliver performance and impact. A passion for, and a literacy in, science and technological innovation is a crucial part of the role. The role will
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Faculty structure Downloading a copy of our Job Description Full details of the role and the skills, knowledge and experience required can be found in the Job Description document, provided at the bottom
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structures, interventions and outcome measures across the research areas of the Faculty. NMPC researchers collaborate in applications for trials of complex interventions, adaptive designs, cluster randomised