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environments. Access proprietary market data and institutional infrastructure, including cutting-edge computing environments and live market simulation tools. Translate research into production, contributing
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interested in connecting spatial and spectral information to understand complex materials systems at the molecular level with machine learning. PhD Student A will work with tumour sections to develop multiple
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materials systems at the molecular level with machine learning. The PhD Student will undertake a study analysing mass spectral imaging data streams in real time using machine learning workflows. A pathway for
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materials systems at the molecular level with machine learning. The PhD Student will work with tumour sections to develop multiple instance learning and weak supervision / spatial transcriptomics models
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dynamics of the pharmaceutical innovation system in each country and the political and economic factors that shaped the way the system operated during COVID-19. This will enable levers to be identified
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to trick AI-based models, pay little attention to fake-normal data traffic generated by Generative Adversarial Networks (GAN). This PhD research will address a major vulnerability in AI based smart grids by