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on developing GaAs PICs in a project that includes everything from III-V laser epitaxy design and simulations, and fabrication, to system level PIC lidar tests and space qualification. Your role and goals You
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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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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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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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, such as sedimentation, meltwater flow, and vegetation change, into active drivers of adaptive design. This interdisciplinary work combines advanced computational tools, including 4D point cloud modeling and
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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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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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skills and finite element modelling simulations. Education, Qualifications and Experience Essential Criteria A PhD (or equivalent experience) in physics, materials science, electrical engineering or
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of the Solar Atmospheric Modelling Suite (SAMS) a next-generation, modular simulation framework for the solar atmosphere, tackling the chromosphere/corona problem. SAMS is being designed to incorporate world
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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