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applying machine learning to a large and diverse, curated clinical dataset. The candidate should have a PhD or MSc in a relevant field such as Neuroscience, Cardiovascular Science, Computer
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clinical research groups at the National Heart and Lung Institute at Imperial College London applying statistical, machine learning and simulation approaches to combine experimental and clinical data with
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clinical research groups at the National Heart and Lung Institute at Imperial College London applying statistical, machine learning and simulation approaches to combine experimental and clinical data with
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The role will engage in cutting-edge translational research that develops computational models for predicting outcomes in cardiac diseases. This includes a machine learning model to rule out heart
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Programme Grant DataSig II, a transformative initiative at the intersection of rough path theory and modern machine learning. This ambitious, multi-institutional collaboration aims to redefine how streamed
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opportunity to design and implement novel digital technologies in collaboration with a wide range of academic collaborators. You will be part of a larger team of Research Associates, PhD students, and a
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. Coordinate modelling activities across multiple projects and deliver high-quality outputs on time. Integrate new methodologies, including AI and machine-learning approaches, into simulation design. Conduct
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opportunity to design and implement novel digital technologies in collaboration with a wide range of academic collaborators. You will be part of a larger team of Research Associates, PhD students, and a
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massive substrate profile datasets in high throughput; application of in-house machine learning algorithms to identify optimal linker activity and selectivity; synthesis and characterisation of linker
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should hold a masters and PhD level qualification in a relevant subject (e.g. Bioinformatics, Computational Biology, Computational Systems Biology, Data Science, Biostatistics, Machine Learning