313 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"BioData" positions at University of Sheffield
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Targets in Amyotrophic Lateral Sclerosis Using Patient-Derived Models and Single-Cell Multiomic Data" Host Institution: University of Sheffield Primary Supervisor: Dr Richard Mead Secondary Supervisors
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Information Grade 6 Salary £32,080 - £36,636 per annum Work arrangement Full-time Duration Open ended Line manager Social Media Manager Direct reports Our website For informal enquiries about this job contact
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treatment window may already have passed. Project Aim: This project proposes the development of an innovative approach that applies computer vision and machine learning to detect early signs of stroke through
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of survey data from service providers in Great Britain. You will work on finalising the survey questions, disseminating the online survey, and collecting and analysing the data. You will also contribute
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an automatic image analysis pipeline to correlate learnings from AFM and super-resolution data. Work closely with collaborators to design an experimental set-up applying the previous tools above to test the
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from partners. Information, Data and Governance Maintain secure filing systems for documents, ethics submissions, approvals and partner correspondence. Support adherence to GDPR, NIHR and HMPPS data
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the project's social media and website, supporting the leadership team's communication with funders, and maintaining all necessary management information and records (e.g., timesheets, project documentation
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to apprenticeships is completed to a high quality and input timely. Contribute to the School’s apprenticeship governance reporting through timely and accurate quality data, presenting data at meetings as required and
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upon ensuring good quality management information is available to allow the team, the wider EFM and its customers to make sound informed decisions, and you will be responsible for presenting this data in
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managed together. The challenge is to develop modelling approaches that can be applied across populations of structures. Instead of building a bespoke model for each asset, information can be intelligently