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of acoustic wave propagation in moving fluid and physics-based machine learning (ML) methods. Support experimental design in the laboratory, carry out data processing and to use the experimental results
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science, computer vision, medical/image analysis is essential. Experience of research (or interest in) in one or more of the following: deep learning; big data management; computational pathology; medical imaging
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/interview) A degree (Bachelor’s, Master’s, or PhD or equivalent experience) in Software Engineering, Computer Networks, Embedded Systems, AI, Data Engineering, or a related subject (assessed at: application
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. To fill in this gap, in collaboration with industrial partners, the research will develop novel Machine Learning and Computer Vision methods for detecting and localising. These will be used to develop
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collaborative programme of research funded by the Aerospace Technology Institute (ATI) with several Industry partners, including Airbus, GKN and Renishaw. Critical for the implementation of additive manufacturing
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are looking for someone with a strong interest in developing novel, unconventional computing systems to tackle complex machine learning tasks at low energy. You should hold a PhD, or be close to submitting, in
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AMRC marketing teams Programme Administration & Support Be the point of contact for students and supervisors, and provide guidance and support on matters related to the PhD and EngD programmes Support
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of Computing at Imperial College London in 2022. Personal website：https://cherise215.github.io/ About the School/Research Group The successful candidate will join the Computer Vision group in the School
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science, computer vision, medical/image analysis is essential. Experience of research (or interest in) in one or more of the following: deep learning; big data management; computational pathology; medical imaging
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collaborative programme of research funded by the Aerospace Technology Institute (ATI) with several Industry partners, including Airbus, GKN and Renishaw. Critical for the implementation of additive manufacturing