10 distributed-algorithms-"Meta"-"Meta" positions at KINGS COLLEGE LONDON in United Kingdom
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will include contributing to our clinical NLP tools, algorithms and interfaces used by clinical specialists. The post holder will be expected to be able to contribute in the following areas: Extend our
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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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will include contributing to our clinical NLP tools, algorithms and interfaces used by clinical specialists. The post holder will be expected to be able to contribute in the following areas: Extend our
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(PSDM) techniques to reduce wiring complexity, and optimise signal demodulation algorithms for accurate, real-time battery monitoring. The position will be based in the Department of Engineering, King’s
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algorithms, models, and their optimization techniques Knowledge of TensorFlow, PyTorch, etc. Effective communication (oral and written) skills, ability to write research reports and papers in style accessible
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study team to help curate high-quality data. The postholders will help support data analysis tasks and work on computational algorithms to help streamline data annotation. Image computing activities
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and accurate registration of ultrasound scans of 3D-printed human skulls to MRI/CT head scans. The research associate will develop anthropomorphic head phantoms and algorithms for fast and accurate
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Experience in devising and developing novel machine learning algorithms Hands on experience with ROS and physical robots Excellent mathematics skills, particularly in areas relevant to robotics and AI
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socioeconomic impacts of artificial intelligence (AI), gender bias in algorithms, critical analyses of fintech, dynamics of platformisation, the emergence of digitally enabled forced labour, biometrics and
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analysis algorithms for the observation and interpretation of existing and new spectroscopic data of exoplanet atmospheres. Experience on cloud/haze microphysics modelling and large scale simulations is