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Field
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systems able to make extremely high time frequency images of roots in situ in the field (https://doi.org/10.1093/jxb/erac427). The entire, multi-person project will interpret the dynamics of root properties
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to support identification of such deepfakes. Using a mixed-method methodology and eye-tracking technology, we will investigate whether people can reliably identify deepfakes (face images and video clips), what
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We invite applications for a fully funded PhD research scholarship in “Unsupervised Machine Learning for Cardiovascular Image Analysis”. This opportunity is available to UK (Home) candidates only
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cornerstone for the application of optics and photonics in a manifold of research and technology fields ranging from optical fiber communications, metrology, environmental and medical sensing and imaging, and
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. Understanding the process of droplet impact and freezing dynamics at high airspeeds, on textured and non-textured surfaces is critical to deciphering the physics behind ice adhesion and accretion. Previous work
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described through five overall research areas: Diagnostic Imaging, Digital Health, Personalised Therapy, Precision Diagnostics, and Sensory and Neural Technology. Our technologies and solutions are developed
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. • Proficient computer skills, including competence in the use of MS Office and other software packages, especially word processing, database and spreadsheet skills. About Swinburne University of Technology
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Qualification type: PhD Location: University of Hull Funding for: UK students Funding amount: £20,780 Hours: Full time Closes: 9 May 2025 Subject areas Engineering & Technology Computer Sciences Mathematics
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of image recognition tools, statistical analyses and the mechanistic description of carbon substrate conversion and growth on solid material. The tasks also include process-related life cycle analyses
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, machine learning, and image processing techniques Strong candidates with electrical, mechanical, and biomedical engineering backgrounds can also apply. To be eligible for this scholarship, you must: Meet