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Reading UK Contract: Full Time, Fixed Term (31/07/2028) Job reference: SRF51665 By reference to the applicable SOC code for this role, sponsorship may be possible under the Skilled Worker Route. Applicants
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By reference to the applicable SOC code for this role, sponsorship may be possible under the Skilled Worker Route. Applicants wishing to consider the SWR must ensure that they are able to meet the
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dissemination of project findings through creative and conventional outputs, for example: reports, infographics, videos, social media, policy briefings and web-based resources. 2) Contributing to, and assisting
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for three years. The project "Smart Efficient Compression: Reliability and Energy Targets (SECRET)" funded by Royal Academy of Engineering and Howden Compressors involves research into improving efficiency
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experience in machine learning and image analysis for ultrasound images and video. The successful applicant will possess specialist experience conducting fieldwork, particularly in low-resource or rural
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qualified in relevant subject area (or nearing completion) Good knowledge of computer vision algorithms for image and video processing Strong experience in machine learning or deep learning for medical image
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Purpose and Values and our Staff Code of Conduct. At Durham we actively work towards providing an environment where our staff and students can study, work and live in a community which is supportive and
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strategies are integrated into the project (e.g., video blogs and academic workshops) and the post holder will be expected to support these activities. You will have: a PhD in cardiovascular or a related field
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collaborative environment. Unable to show this video due to your cookie preferences
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. For additional information, kindly contact Prof. Iiro Harjunkoski, iiro.harjunkoski@aalto.fi . Want to know more about us and your future colleagues? You can watch these videos: Aalto University – Towards a