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This research project aims to establish the theoretical and algorithmic foundations of quantum adversarial machine learning, an emerging field at the intersection of quantum computing and machine learning. It
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algorithmic foundations of quantum adversarial machine learning, an emerging field at the intersection of quantum computing and machine learning. It investigates how the unique capabilities of quantum computing
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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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river system Develop, test and apply algorithms for the processing and analysis of satellite data drawing on the latest physics-based and/or data-driven techniques Contribute to work on the automation and
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work alongside renowned academics and researchers in ENU’s School of Computing, Engineering and the Built Environment. If you are someone with expertise in multimodal speech processing and AI algorithms
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(SDR) platforms and characterise them in the presence of interference in a variety of spectrum sharing scenarios, seeking opportunities for algorithms which provide enhanced interference resilience
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systems on software defined radio (SDR) platforms and characterise them in the presence of interference in a variety of spectrum sharing scenarios, seeking opportunities for algorithms which provide
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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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institutions, and leading industry partners. The successful candidate will contribute to the delivery of high-impact research projects involving AI algorithm evaluation and image data analysis. You will play a