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on quantitative phenotyping via generative modelling of quantitative MRI data. This exciting PhD position combines advanced machine learning with medical imaging physics to develop next-generation tools
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combines advanced machine learning with medical imaging physics to develop next-generation tools for biomarker extraction and clinical decision support. You will develop innovative generative models using
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supporting better patient outcomes. The successful candidate will lead the development of multi-modal MRI foundation models that integrate imaging data and radiology reports. Using advanced deep learning
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to develop novel computational methods for data integration and analysis Experience with machine learning approaches for biological data modeling and predictive analytics Good communication skills and
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are looking for candidates to have the following skills and experience: Essential criteria- Lecturer PhD in computer science or related field. Ability to teach undergraduate and postgraduate modules in core
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metabolism Strong problem-solving skills and the ability to develop novel computational methods for data integration and analysis Experience with machine learning approaches for biological data modeling and
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are seeking a postdoctoral research associate to lead an innovative EU-funded project at the intersection of polymer chemistry, computational modelling, and machine learning. The primary role is to develop a
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experimental chemistry, providing a supportive research environment. Applicants should have a PhD in Chemistry or related field, and extensive experience in python programming and machine learning models
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of machine learning to metamaterials modelling Experience in modelling molecular interactions Experience in modelling of mass transport at the nanoscale Downloading a copy of our Job Description Full details
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for analysis of large-scale bulk and single cell data sets Strong understanding of statistical modelling, data normalisation and machine learning methods applied to biological datasets Experience with data