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Federated learning (FL) is an emerging machine learning paradium to enable distributed clients (e.g., mobile devices) to jointly train a machine learning model without pooling their raw data into a
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distribution can predict both single AZ and whole synapse function at the neuromuscular junction (NMJ). Adapt and convert an existing, experimentally validated MCell simulation of mammalian NMJ AZs into Python
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control laws into Trent gas turbine engines and developed algorithms monitoring fleets of 100s of engines flying all around the world. During the PhD, you will have the opportunity to deeply engage with
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computational tools for quantifying electromagnetic field distributions down to the fundamental atomic scale. The project will build on recent developments in inverse scattering methods, including ptychography
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individually, make a real difference. The role To research and develop software modules, tools and API for a project that aims to create a secure decentralised data marketplace based on distributed ledger
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system, including a suitable camera and an embedded system, will enable real-time acquisition of images of the tube carriage, from the side of the carriage, and subsequent image processing algorithms
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and its implementation in distributed systems. Main responsibilities: Research, develop, and optimise machine learning algorithms, including deep learning, for AV control and coordination. Apply multi
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to research-based activities, including the development of new data analysis algorithms, processing and analysis of field data, and participation in the fieldwork. Your responsibilities will include: Conduct
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(Preferred) Stay informed about video marketing trends and experiment with new techniques to improve engagement Optimise video content for search engines and social media algorithms to maximise reach Manage
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training. His research focuses on developing algorithms to predict gene targets and target tissues affected by chemical exposures, as well as investigating how environmental exposures influence human health