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research projects investigating novel strategies, discovered within our group, to combat heart failure. The work will integrate studies using isolated cell systems with advanced in vivo models, with a
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position in computational biology combines the development of novel computational methods involving control theory with practical systems modelling, simulation, and data analysis, applied first and foremost
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computational methods involving control theory with practical systems modelling, simulation, and data analysis, applied first and foremost to the field of platelet biology and haemostasis. The postholder will
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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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About us The Green Laboratory investigates tissue morphogenesis and the action of morphogens. We use animal models to investigate the physical morphogenesis of tissues. It is part of the vibrant and
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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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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
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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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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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compartments. The successful candidate will combine cutting-edge in vitro and in vivo infection models, and both microscopy and flow cytometry-based readouts of infection to develop their independent research