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scientific publications, patents, and seeing collaborators translate our work into real-world settings. You will be responsible for developing machine learning and AI algorithms for a range of data and
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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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algorithms is desirable. About the Department This post is within the School of Engineering and Materials Science, a large School with 108 academics, more than 250 PhD students and over 2500 undergraduate
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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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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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. 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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, 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