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EPSRC-funded project, MAPFSI that will be focused on developing experimentally-validated computational algorithms for fluid-structure interaction problems including multiphysics effect of electromagnetism
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into real-world settings. You will be responsible for developing machine learning and AI algorithms for a range of data and applications (e.g. natural language processing, multivariate time-series data
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, leading and delivering innovative research to characterise and understand the search landscape and algorithm dynamics induced by combining complex optimisation problems and methods. Description of Duties
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representation, knowledge engineering, linked data. About the role The successful candidate will join the Distributed AI (DAI) group in the Department of Informatics, King’s College London. They will carry out
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. You will focus on developing microwave techniques and associated electronics to precisely control the curing process, using AI-based algorithms to optimise outcomes. Full support will be provided
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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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immunofluorescence imaging. The successful candidate will support the design, optimisation, and analysis of multiplex panels to characterise the spatial distribution of immune and other cells within tumour tissues
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, delivering tested methods, and creating algorithms to expand MMFM capabilities across domains like cardiology, geo-intelligence, and language communication. The postholder will help lead a project work package
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to the development of innovative and sustainable low carbon plastic waste management and recycling solutions. In this project the post holder will develop novel algorithms and methods for analysis of plastic data