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logging into their existing Workday employee account. Position Summary: The Postdoctoral Fellow will conduct coupled physical-biogeochemical modeling to understand harmful algal blooms (HABs) in subtropical
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. Developing predictive models for precision oncology: We leverage large-scale multimodal datasets, including molecular profiling, clinical records, imaging, and longitudinal treatment histories, to develop
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; workforce of the future; and accessible Innovation facilities. This particular project will use a combination of in vitro models of the human colonic microbiota, small-scale human dietary interventions
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foundational methods for integrating single-cell and clinical transcriptomes; and train, fine-tune, and validate deep learning models using multi-omics and imaging data to predict clinical outcomes such as
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induced local pH environments develop in electrochemical systems; develop theoretical and computational models to predict local pH conditions in complex water matrices and investigate the impact of water
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scenarios for achieving a low-carbon society in 2050. A key ambition of the project is to support scenario-based planning and backcasting. Rather than predicting a single future, the modelling framework
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predict interaction effects. Unlike robot-specific neural network models, the proposed approach aims to learn a universal representation of local interactions (fluid-structure, robot-robot, robot-object
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investigates the thermodynamic modelling of chloride migration in concrete exposed to accelerated chloride ingress conditions, with particular focus on replicating and predicting the results of the NT Build 492
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models, retention strategies, and student support services; of higher education policies, procedures, and regulations related to academic progression, student appeals, and degree completion; of data
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developing and applying these approaches to advance in vitro and ex vivo experimental models relevant to pharmaceutical performance assessment is desirable, particularly methods that support mechanistic