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the regulation of capsule expression during transition between host compartments. The successful candidate will combine cutting-edge in vitro and in vivo infection models, and both microscopy and flow cytometry
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Multiphysics software (COMSOL and/or Abaqus), python/matlab coding and Molecular Dynamics Simulator (LAMMPs). 4. Publications in mechanical metamaterials, soft robotics, or related area in peer-reviewed
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networks. The research will employ mathematical modelling and computer simulation to identify synaptic plasticity rules which enable effective learning in large and deep networks and is consistent with
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experience in: Deep learning Medical imaging computing (preferably neuroimaging) Computationally efficient deep learning Deep learning model generalisation techniques. Translating deep learning models
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and experience: Essential criteria PhD in bioinformatics, computational biology, or a related discipline * Extensive experience and expertise in analysing/ training models on biological or chemical
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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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models and/or human samples Experience with eukaryotic cell culture systems Downloading a copy of our Job Description Full details of the role and the skills, knowledge and experience required can be found
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vitro and in vivo infection models, and both microscopy and flow cytometry-based readouts of infection to develop their independent research project and will also be expected to collaborate more widely
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simulation, fabrication and experimental characterisation. The successful candidate will also be expected to contribute to the formulation and submission of research publications and research proposals, as
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the control and stability of quantum operations. Using advanced Multiphysics simulation tools, the researcher will create models of the physical and control architecture, enabling the identification of design