367 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" positions in United Kingdom
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leading School. In this role, you will: Teach at undergraduate and master’s level, including lectures, seminars, tutorials and computer labs. Act as a personal tutor, providing academic and pastoral support
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different environments influence behaviour and wellbeing. Advanced analytics, including AI and machine learning, will be used to interpret behavioural and emotional data, enabling real-time insights
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students and supporting their academic development. In this role, you will: Teach undergraduate students through lectures, seminars, tutorials and computer lab sessions. Act as a personal tutor, providing
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the requisite experience. Desirable: B1 An awarded (or recently submitted or near completion) PhD in subject specialism or equivalent. B2 Experience with high dimensional statistical, machine learning and deep
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, machine learning, multiscale and multiphysics simulation, computational anatomy, medical image analysis, and integration of wearables and biosignal processing, applied to conditions ranging from cardiac
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network management. You will have a good first degree in physics, electrical and electronic engineering, computer engineering/science, or another relevant subject, and a PhD or equivalent experience in
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research and support the development and learning of the scientists & engineers of tomorrow? If you are, we would love to hear from you. You will provide essential specialist technical support to researchers
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Intelligence and machine-learning approaches and emerging digital technologies such as non-contact sensors, smartphones, and computer tablets. This theme could also include research in data analytics and
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. Neuromorphic sensing, e.g. astronomical imaging (https://www.optera.au/) Software verification tools for space robotics. Electric Propulsion & Space Debris mitigation The University actively fosters a culture of
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., Ntoa, S., Salvendy, G. (eds) HCI International 2023 Posters. HCII 2023. Communications in Computer and Information Science, vol 1833. Springer, Cham. https://doi.org/10.1007/978-3-031-35992-7_2 Familoni