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experimental approaches to develop and validate novel in vitro and ex vivo approaches that model arterial medial calcification without using any animal products. This work will represent an exciting step forward
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to projects involving designing, conducting, and disseminating health economic studies using prospective research study or routine data, decision analytic modelling, health outcome assessment methods and other
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responsibility for implementing a deep learning work-package as part of a Cancer Research UK-funded programme, developing an image-recognition model to identify morphological features corresponding to clonal
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Research Council’s (AHRC) Bridging Responsibilities AI Divides (BRAID) programme that will explore new technologies, new business models and new approaches to data provenance in pursuit of an equitable
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)genetic perturbations and mouse in vivo models to investigate the contributions of tissue-specific gene regulation and non-coding GWAS signals to cardiac traits and diseases (Frost et al bioRxiv 2025, Parey
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complex study. Applicants must have a PhD in a relevant subject. The study requires substantial skills in cell and molecular biology and will require vivo testing of the newly created cell-models. Omics
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different epidemiological scenarios and study designs using theoretical models. By confronting these models with high-resolution, longitudinally sampled metaviromic data from wild rodents in differing
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approaches, and are excited to work on an innovative research project seeking to move forward vaccine design against malaria-causing parasites, then please check out the job description and person
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applications using an existing agent platform Evidence of a publication record in good quality journals Experience/expertise with computational modelling of agent models and behaviour. Practical experience
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. The successful applicant will work with Professor Christian Beck to develop stochastic models for measured frequency data, to construct superstatistical stochastic differential equations with the same statistical