180 data-"https:" "https:" "https:" "https:" "https:" "https:" "Dr" "UCL" "UCL" "UCL" uni jobs at King's College London
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the section of Perinatal Psychiatry at the Stress, Psychiatry and Immunology Lab. As part of this role, they will be a study coordinator for HappyMums, a consortium study which investigates whether data
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information is shared across King’s and with external stakeholders. The role is varied and unique, offering an opportunity for an initiative-taking individual to lead on operational delivery, whilst working
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and recommendations, drawing on a variety of perspectives and sources, and supported by robust analysis Synthesising and analysing a variety of quantitative data, including working closely with
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or similar areas of work (e.g. contracting, business development, operation support, finance/accounting etc.) Experience and/or knowledge of using databases, management information systems and Excel Experience
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grade 7. Visit the Centre for Research Staff Development for more information. About You To be successful in this role, we are looking for candidates to have the following skills and experience
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. This role is based within the Faculty of Life Sciences and Medicine, in the Department of Infectious Diseases, which brings together expertise across microbiology, immunology, clinical sciences, data science
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Technicians, Teaching Fellows and AEP equivalent up to and including grade 7. Visit the Centre for Research Staff Development for more information. About You To be successful in this role, we are looking
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and the skills, knowledge and experience required can be found in the Job Description document, provided at the bottom of the page. This document will provide information of what criteria will be
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the ability to draft documents and correspondence Excellent organization and time management skills Strong numeracy skills and ability to analyze complex numerical data Excellent working knowledge of Microsoft
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’ to ablate. Here, we aim to further develop, clinically validate, and prospectively evaluate, a novel in-silico tool that uses patient imaging data to reconstruct personalised ‘digital twin’ cardiac models