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detection, segmentation, classification, and pose estimation, as well as integrating these models into real-time systems. The role will involve working with large and multi-modal datasets (e.g., images, video
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experience in geo-spatial analysis and working with remotely-sensed satellite imagery Demonstrable experience of image analysis (for example, using Google Earth Engine or PlanetScope) Experience of programming
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) and the fellow is expected to visit them in Cambridge regularly. The successful applicant will be expected to independently develop a crystal growth set up which uses imaging and image analyses
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vision for future research and practice in the visual and material medical humanities. We understand visual and material to refer to both artefacts (including but not limited to images, objects and
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) to predict what we should see and where we should see it in real-world scene (images of everyday environments). More specifically, it examines to what extent and how predictions about scenes can be updated
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electronics, development of FPGA devices, development of narrow-linewidth lasers, laser frequency stabilisation and control, image analysis, data acquisition, optical design and Gaussian beam propagation
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the whole MC-LFPE system. The research project is led by Professor Yaodong Wang. The successful candidate will help develop such a new prototype MC which can be coupled with the LFPE. The project is being
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of the line manager. To design and develop novel and cutting-edge deep learning and image processing frameworks for automated species identification and environmental stress assessment, as well as frameworks