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-seq datasets, and applying advanced statistical and machine-learning methods (AI/ML) to extract novel biological insights that drive our translational and fundamental research programmes. In
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will work closely with the Principal Investigator (PI), Co-PI, and the research team to develop deep learning-based computer vision algorithms and software for object detection, classification, and
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testing of machine learning/AI algorithms Integration of radiomic and biological datasets Working closely with Medical Physics colleagues on reviewing recommendations for detection of specific metabolites
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tools such as R, Python, or MATLAB as well as relevant machine learning frameworks Experience in statistical data analysis, and expertise in areas such as experimental design, linear/nonlinear models
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, specifically modelling burrowing behaviour and its impact on the skull. Responsibilities include conducting a range of computer simulations using discrete element and finite element methods, as
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spatial transcriptomics and imaging genomics projects, integrating bulk and single-cell RNA-seq datasets, and applying advanced statistical and machine-learning methods (AI/ML) to extract novel biological
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10 minutes and machine learning algorithms to deliver quantitative diagnosis without destroying the samples. The AF-Raman prototype will be integrated and tested in the operating theatre
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developing machine learning or data science approaches for patient stratification and genetic association analyses using cardiac magnetic resonance imaging in biobank populations. Successful applicants will
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themes are not covered, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML
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themes are not covered, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML