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Biological Learning Machine, which is headed by Professor Jan Østergaard. The goal is to develop novel information-theoretic methods for identifying and analyzing temporal and spatial patterns of synergy and
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of automatically predicting the veracity of textual claims using machine learning methods, while also producing explanations about how the model arrived at the prediction. Automatic fact checking methods often use
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or Nextflow A willingness to learn and apply machine learning approaches Offer A doctoral scholarship for a period of 1 year to start, with the possibility of renewal for a further three-year period after
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analysis Background in biomedicine and digital pathology What we offer Embedding within a computational team, with extensive experience in computational biology and machine learning. Embedding within
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analysis Background in biomedicine and digital pathology What we offer Embedding within a computational team, with extensive experience in computational biology and machine learning. Embedding within
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breakdown spectroscopy (LIBS) and Raman spectroscopy) on metals and impurities • Development of a miniaturized laboratory setup for combined LIBS and Raman spectroscopy • Advanced machine learning
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PhD: Systematic Exploration of Robot Behaviours for Manufacturing Tasks to Automatically Discover Failure Scenarios EPSRC Centre for Doctoral Training in Machining, Assembly, and Digital Engineering
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parameters to identify regimes that ensure both flame stability and low pollutant emissions. Machine learning techniques have recently shown promise for Design of Experiments (DoE) and interpretation of large
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shifts in cell state and cell fate. Integrate spatial transcriptomics data to anchor these predictions in tissue context. Develop machine learning methods (e.g. graph neural networks, variational
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of machine learning to evaluate the predictive value of biomarkers from various sources: donor-related data, perfusion fluid, and kidney biopsies. Kidney biopsies may contain unique information about organ