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Modeling naturally fractured reservoirs is re-gaining interest in the Oil & Gas industry and academia for application in carbonate fractured reservoirs and unconventional reservoirs where natural
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, and utilize the state-of-the-art multi-scale atmospheric models, along with measurement and statistical approaches, to understand the mechanisms that drive the climate-air quality interactions, and
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CRISPR tools to correct or model the defective genomic DNA, and further deliver these newly-engineered editors into targeted organs to correct the corresponding phenotypes. The current disease models
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to rigorously evaluate new and uncertain community-level interventions in infectious disease control. A key component relates to harnessing advanced mechanistic disease transmission models to improve prediction
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specific responsibilities in the project: i. Develop, train, and optimise deep learning models for object detection, classification, and segmentation using real-world datasets. ii. Design and implement
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researcher in natural language processing and large language models to work with a team from multiple disciplines of machine learning and artificial intelligence to develop multimodal large language models
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targets and biomarkers. Study cardiomyopathy across models of diabetes, cancer, and pressure overload. Collaborate in an interdisciplinary team and contribute to high-impact publications. Your
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, machine learning, mathematical modelling, or a related field, to join our research team in the Department of Applied Health Sciences. The successful candidate will work on an NIHR funded methodology project
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development and regeneration. Our research combines in vivo genetic models, in vitro organoid systems, advanced imaging, and high-dimensional sequencing approaches to uncover fundamental principles by which
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methods for use across developmental biology and more widely. Current activity involves work on the following aspects: development of new techniques for assessing and modelling the states and dynamics