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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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on the use of set-up of the in-house randomisation system. Teach statistics in tutorials to undergraduate and postgraduate students in the school Contribute to the delivery post-graduate teaching where
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Education Institutions. Preference factors: Experience in publishing scientific articles experience in deep learning experience in processing and analyzing biomedical data. Minimum requirements: MSc grade
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pipelines. Your primary focus (80% of your time) will be on leading spatial transcriptomics and imaging genomics projects, integrating bulk and single-cell RNA-seq datasets, and applying advanced statistical
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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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annotation of these metabolomes using multistage fragmentation (MSⁿ) data, incorporating novel computational methods and strategies (e.g. spectral matching, network-based approaches, machine learning) where
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involving speech/audio, images, and video along with text-based applications. Job Requirements: MSc (Research Associate) or PhD (Research Fellow) in Electrical Engineering, Computer Science, Statistics
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annotation of these metabolomes using multistage fragmentation (MSⁿ) data, incorporating novel computational methods and strategies (e.g. spectral matching, network-based approaches, machine learning) where