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. Traditional methods struggle with real-time network traffic, especially on lightweight, low-power, resource-constrained edge devices. This project aims to explore the potential of spiking neural networks (SNNs
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Neural Network hardware. -> You will be part of a dynamic team in Manchester with access to specialised laboratory space for thin film deposition and magnetic/electrical/high frequency characterisation and
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include but not limited to Statistical-computational gap in modern machine learning Robustness of neural networks for trustworthy ML systems Fine-tuning of modern machine learning models The successful
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technical background in mathematics and statistics, including Bayesian modelling is essential. You will also have demonstratable experience in Deep Neural Network based approaches, coding skills in Python and
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neural network based methods for designing genome editing strategies to treat rare diseases. The postholder will join a large multidisciplinary team covering molecular methodologies, genomics, single cell
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, to develop a novel end-to-end neuromorphic design approach and efficient hardware architecture based on spiking neural networks (SNNs). The project aims to develop novel computing solutions for the defence and
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techniques to enable intelligent control in Open Radio Access Network (O-RAN) systems. Based at the University of Bristol and fully funded by EPSRC and BT for 4 years, this PhD project sits at the intersection
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of Oxford. You will be expected to conduct novel research in geometric ML, in particular on graph neural networks and geometric generative models and applications to molecular design. You will also provide
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aureus (Orazi et al. mBio 2019). You will apply state-of-the-art, machine learning methods (deep neural networks and evolutionary algorithms) on big-data from thousands of individual bacterial (Lapinska et
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Project title: Machine Learning models for subgrid scales in turbulent reacting flows Supervisory Team: Temistocle Grenga, Ed Richardson Project description: Supervised deep convolutional neural