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for computation. This is especially useful for distributed and federated AI, where the input of one node is often a function combining the output of many other nodes. AirComp offers a significant scalability and
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(HPC) platforms used in machine learning, big data and artificial intelligence (AI) based applications (CPUs, GPUs, AI accelerators etc.) require high power demands with optimized power distribution
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Project advert Madagascar is a globally important biodiversity hotspot, home to hundreds of endemic species. At juxtaposition with this rich natural heritage is a growing human population and a
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technologies used in and connected to grids (e.g., distributed generation, storage, electric vehicles, heat pumps, smart energy management systems, etc.) and how to design grid tariffs that align grid and market
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persists, even for the most powerful sensors operating in this way. A drastic departure from this sensing architecture is “multistatic” radar – enacted by a coherent network of spatially distributed sensors
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achievement of net-zero greenhouse gas emissions targets. Because of this, peatland restoration is a conservation priority in the UK and internationally. However, as ecosystems dependent on the maintenance
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architectures and distributed storage integration. Examining the physical arrangement, fire safety, redundancy, and maintenance requirements for embedded storage. Evaluating economic considerations, including
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Funding for: UK/Home Students We invite applications for a fully funded PhD research scholarship in “Unsupervised Machine Learning for Cardiovascular Image Analysis”. This opportunity is available
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Location: Students will be based at the CREWW building on Streatham Campus in Exeter. Flooding is the most common natural disaster, impacting billions worldwide. Natural Flood Management (NFM), a
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computational tools to support the safe and ethical deployment of AI in clinical settings. The research focus is on AI performance monitoring, distribution shift detection, bias assessment, and stress testing