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Field
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-organising reaction networks and the emergent complexity in such systems form powerful reservoir computers capable of non-linear classification, times-series prediction and forecasting, on a par or even
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, covering all cardiac conditions. This makes them unsuitable for identifying rare or complex cases, where annotations are scarce or unreliable. Recently developed unsupervised learning methods allow
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Multiple PhD Scholarships available - Cutting-edge research at the frontiers of Whole Cell Modelling
movements across the cell envelope, to help inform mathematical models that integrate all of the envelope’s function. Uncovering RNA-RNA Networks in the Regulation of Bacterial Surface Proteins: Bacterial
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effort at the intersection of machine learning and applied mechanics. The focus of this position is on extracting information about what a neural network has learnt in a symbolic and (human) interpretable
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testing) to understand and tailor the physical and chemical interactions within these complex structures. Cranfield University is internationally renowned for its research into materials for extreme
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workshops, career development, and networking Join a dynamic institute hosting over ten atmospheric chemistry research groups, offering rich opportunities for collaboration and learning Extensive
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professional network spanning academia, industry, and national research centres. Through this multidisciplinary project, the student will develop expertise in: Contribute to the development and operation of
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The Child and adolescent Health Impacts of Learning Indoor environments under net zero (CHILI) Hub is a program funded by the MRC and NIHR, the goal of which is to understand the health effects we
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Your Job: The main objective of this PhD project is to achieve a better understanding of the efficient propulsion of trypanosomes through complex crowded environments, mimicking biological tissues
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reliability and operational efficiency. Determining the optimal size and location of PSTs within a network is inherently complex due to the nonlinear and dynamic nature of power systems, necessitating the use