25 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"FCiências" Postdoctoral 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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applicant will be able to present information on research progress and outcomes, communicate complex information, orally, in writing and electronically and prepare proposals and applications to external
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About Us The Centre for Rheumatic Diseases at King’s College London is an internationally recognised Centre of Excellence for rheumatology research, spanning health data science, clinical trials
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are not limited to: natural language processing, large language models, graph learning, prompt engineering, knowledge graphs, knowledge engineering, linked data, web technologies. About the role
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PDRA will contribute to the development of a database of high-resolution equity indicators and apply cutting-edge GeoAI and spatial data science techniques to model and classify population health risk
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experience in quantitative data analysis, you will examine the links between mental distress and work, care and welfare. You will take forward some selected pre-existing projects (e.g. on the relationship
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“Data-driven design of organic mixed conductors”. Mixed ionic/electronic conducting materials are being developed for applications in bioelectronics, biosensing, energy storage and neuromorphic computing
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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 for candidates to have the
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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 Fluency in English Strong skills in
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of Biomedical Engineering & Imaging Sciences. About The Role The research associate will lead the development of cutting-edge multi-modal MRI foundation models. These models will leverage both imaging data and