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We are looking for a motivated and talented postdoctoral-level researcher with experience in executable modelling to join a cutting-edge project developing Digital Twins for rare diseases. This is a
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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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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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pathogens. The successful candidate will make extensive use of a range of in vivo models of fungal infection and will have access to the wide range of transgenic tools and in vivo immunology techniques
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pathogens. The successful candidate will make extensive use of a range of in vivo models of fungal infection and will have access to the wide range of transgenic tools and in vivo immunology techniques
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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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Research Fellow (Fusion Shielding Materials) - School of Metallurgy and Materials - 105638 - Grade 7
variety of research methods, such as scientific experimentation, literature reviews. Analyse and interpret data Contribute to developing new models, techniques and methods Disseminating research results
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resting conditions. The researcher will use a combination of synovial tissue organoid systems and transgenic mouse models to delineate the role of the proteoglycan-4 (the gene that encodes lubricin) in
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Climate Research (CICERO, Norway) will work together to will address challenges in separating SRM signals from other factors, improving climate models, and attributing climate responses of SRM