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researchers to translate these experimental results into our numerical models, helping to improve their predictive capability. You will help ensure a healthy and vibrant research environment within the Impact
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an industry partnered project for translational drug discovery. The role will involve analysing large scale omics and spatial datasets from both primary patient samples and advanced in vitro model systems
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an industry partnered project for translational drug discovery. The role will involve analysing large scale omics and spatial datasets from both primary patient samples and advanced in vitro model systems
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/weather observational and modelling products would be of a substantial value. Furthermore, experience with epidemiological modelling and/or attribution of extreme events and their impacts in a changing
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for the game changing impact of our science globally. Click here to find out more about working at the John Innes Centre. About the Howard Group: The Howard group combines mathematical and biophysical modelling
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complex behaviours, including learning, in the small invertebrate model organism C. elegans. We have recently discovered a range of novel dopamine receptors in C. elegans and found that different receptors
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and/or flow cytometry-based readouts of infection Experience with biochemical assays (e.g. Western blotting) Experience handling/processing samples in vivo infection models and/or human samples
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dedicated to development, translation and clinical application within medical imaging and computational modelling technologies. Our objective is to facilitate research and teaching guided by clinical
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of Engineering, to work on an EPSRC funded project – ‘Engineering Twist'. The project aim is to develop experimental approaches to characterise enriched continuum models for mechanical metamaterials, and their
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these bioinformatic experiments. Access to a high-performance computer will be provided. The candidate must be capable of generating complex molecular compound models in silico and using current molecular dynamic