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are passionate about applying machine learning to real-world clinical challenges. The successful candidate will lead the development and validation of predictive models using multimodal data including neuroimaging
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. Apply by uploading your generic CV and research proposal through AJO. For further information please contact Malcolm Fairbairn (Malcolm.fairbairn@kcl.ac.uk ). Salaries (including London weighting) will
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, for example when processing data or preparing manuscripts and presentations. About You To be successful in this role, we are looking for candidates to have the following skills and experience: Essential
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: Essential criteria PhD awarded in cardiovascular related research * Experience in cardiac MRI data acquisition and analysis in animal models of heart disease Experience with preclinical research models
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Charter, Stonewall Diversity Champion, and Disability Confident Employer. We are also proud signatories of the Armed Forces Covenant. Further Information For informal enquiries, contact Dr Rupam Das
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, provided at the bottom of the page. This document will provide information of what criteria will be assessed at each stage of the recruitment process. Please note that this is a PhD level role but candidates
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analysis, medicine development, medical analysis or medical device development Evidence of strong laboratory and clinical skills Good communication skills Experience of scientific report writing and data
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computing. You will join the Vision & Human-Robot Interaction (VHR) Group, which brings together researchers working at the intersection of computer vision, robotics, and assistive technologies. The team is
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Teaching 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
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, 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 assessed at each stage