171 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"Dr" positions at King's College London in United Kingdom
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an exciting opportunity for a Postdoctoral Research Associate to join their dynamic, interdisciplinary team to shape how multimodal bioimaging data is stored, shared and reused. The Parsons Group is based in
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and strengthens financial and regulatory compliance with responsibilities spanning both PCI DSS (Payment Card Industry Data Security Standard) and counter fraud activity. The post holder supports
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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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Development for more information. About You To be successful in this role, we are looking for candidates to have the following skills and experience: Essential criteria PhD qualified in Pharmacology or other
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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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journeys Adopt a user centric approach Elicit user needs Desirable criteria Competitor reviews Best practice reviews Leading UX persona workshops Information architecture / digital strategy
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health. The post holder will develop innovative AI techniques approaches applied to oral healthcare, with a particular focus on Large Language Model-based multimodal data understanding and reasoning
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judgement when handling confidential information. Desirable criteria Educated to degree level or equivalent Downloading a copy of our Job Description Full details of the role and the skills, knowledge and
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releasing confidential information obtained during the course of employment to those acting in an official capacity. Downloading a copy of our Job Description Full details of the role and the skills
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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