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conditions. Our work combines traditional statistical methods with advanced artificial intelligence algorithms to identify patterns in disease. We also use qualitative methods to understand lived experiences
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or more of: the use of micro/nanofabrication and materials characterization tools; computational multi-physics/electromagnetics modelling and/or the application of machine learning algorithms; experimental
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based at the School of Electronics and Computer Science, Southampton. The project is researching, developing and evaluating decentralised algorithms, meta-information data structures and indexing
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conditions. Our work combines traditional statistical methods with advanced artificial intelligence algorithms to identify patterns in disease. We also use qualitative methods to understand lived experiences
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diverse workforce, inclusive of people of all ages and beliefs, from different racial, educational and social backgrounds opens up a wealth of possibilities, makes us more creative and accelerates our
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assimilation, and multiple data sources to address two key research questions about water-energy interactions at biophysical scales: What are the scales of SM variability across different landscapes and their
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at different scales including cities, towns and districts. More information about ECCD projects can be found at www.energy.soton.ac.uk We offer a generous renumeration package, an attractive research environment