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Field
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-Brain inspired Neuromorphic Nanophotonics (InsectNeuroNano). The long-term vision of this project is a novel on-chip hybrid nanostructure platform for energy-efficient, fast artificial neural networks and
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identity, behavior, and trajectory. You will extend these models to understand how genetic risk factors alter cell–cell communication networks. Spatial transcriptomics datasets will be used to anchor
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sensors of the future, whilst also setting the foundations for the software technologies to run on this new generation of equipment – which of course includes AI. Meanwhile we are pushing the limits
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future-proof. At the same time, we are developing the chips and sensors of the future, whilst also setting the foundations for the software technologies to run on this new generation of equipment – which
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are developing the chips and sensors of the future, whilst also setting the foundations for the software technologies to run on this new generation of equipment – which of course includes AI. Meanwhile we
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the electricity grid, which our faculty is helping to make completely sustainable and future-proof. At the same time, we are developing the chips and sensors of the future, whilst also setting
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point-of-care spectroscopic sensor to determine the impact of the cell therapy during orgran perfusion. This includes generating experimental data sets with a commercially available spectrometer
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: Develop an event-driven RL algorithm that sparsely updates network state and parameters that will significantly improve energy to-solution efficiency compared to conventional digital accelerators when
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. Machines must be equipped in-situ with smart sensors and supported by systems that can process such as images and time series, in real time. Machine learning and AI have become essential for driving
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simulation of photonic systems, sensor systems, signal processing and device manufacturing, development of machine learning algorithms, and design of optical communication networks or power consumption and