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to capture the spatial complexity of tumor organization and its relationship to treatment response. This PhD project aims to develop robust multimodal predictive models of platinum resistance using a large
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. The Postdoctoral Associate will work under the direction of the Principal Investigator in building a first-of-its-kind Software as a Medical Device (SaMD) that predicts, detects, and manages SSIs by fusing RGB
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research fellows to join a multi-year research initiative sponsored by the Bezos Earth Fund . This project aims to develop and deploy advanced AI-driven learning, prediction, and decision-making tools
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longer-lasting charging strategy for Li-ion cells using two complementary approaches. (1) By testing commercial cells under various controllable stress factors and integrating lifetime prediction models
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: Compiling and analyzing large erosion data sets (thermochronology, cosmogenic nuclides, suspended sediment, etc.); Statistical modelling of data to analyze drivers and make local and/or global predictions
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approaches treat NP design as static property prediction. This project takes a fundamentally different approach: using generative models to propose novel NP formulations and coupling them with explainability
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of hybrid foundation model-graph neural network architectures for gene perturbation prediction, including the design and implementation of novel training strategies under experimental constraints, e.g
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incorporate clinical, lifestyle, and nutritional factors to build predictive models through advanced bioinformatics and machine learning. By identifying molecular signatures that distinguish responders from non
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FLAME-GPU accelerated agent-based modelling of material response to environmental and operational loading EPSRC CDT in Developing National Capability for Materials 4.0, with the Henry Royce
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required for the project or the hosting universities. This full-time 3 year PhD studentship focuses on the use of technology to assess symptoms of PD and for PD prediction. The key aim of this PhD is to