21 data-"https:"-"https:"-"https:"-"https:"-"Computer-Vision-Center" Postdoctoral positions at King's College London
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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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members in data analysis best practice and accurate record keeping and will be encouraged to present at international meetings and publish in high-impact journals. A major component of the position will be
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institutions, you will take a prominent role in coordinating research activities, disseminate information and contribute to creative outputs. You will engage in all aspects of the project, including
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, using genomic and clinical data from large cohort studies on immune-mediated inflammatory diseases including psoriasis and rheumatoid arthritis. The PDRA will lead on research across all three objectives
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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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offers well before the January 30th agreed deadline for HEP postdocs. Apply by uploading your generic CV and research proposal through AJO. For further information please contact Malcolm Fairbairn
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/experimental design and analysis of complex research data. Advanced knowledge of preclinical (in vitro and/or in vivo) validation. Expertise in small animal imaging Possess excellent written and verbal
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, Research and 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
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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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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