94 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"NOVA.id" positions at King's College London
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lead the development of multi-modal MRI foundation models that integrate imaging data and radiology reports. Using advanced deep learning techniques—including vision-language architectures (e.g., CLIP
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Hub supports the integration of digital education and technology-enhanced learning (TEL) across the faculty and collaborates with the Florence Nightingale Faculty of Nursing, Midwifery & Palliative Care
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International Education and makes a significant contribution to the rich and diverse make-up of the King’s student body. About the role The Learning Technology Officer will support the implementation of Digital
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looking to appoint a Reader in Computer Vision Education. This is an exciting time to join us as we continue to grow our department and realise our vision of a diverse, inclusive and innovative department
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About us: We are seeking to appoint a postdoctoral research associate with an excellent track record in semantic technologies and machine learning. Topics of interest in this area include, but
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new Wellcome-funded Imaging Machine learning And Genetics in Neurodevelopment (IMAGINE) lab, in the Research Department of Biomedical Computing. The post will benefit from the extensive and broad
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innovative EU-funded project at the intersection of polymer chemistry, computational modelling, and machine learning. The primary role is to develop a complete in silico framework to accelerate the discovery
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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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, the role will also work closely with the wider Digital Education community across King’s. The Senior Digital Education Officer will support the administration of King’s Moodle-based virtual learning
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are caused and how people respond to treatments. We will investigate blood and cerebrospinal fluid from patients and use machine-learning to identify the treatment-relevant immune processes, with an emphasis