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Funding for: UK/Home Students We invite applications for a fully funded PhD research scholarship in “Unsupervised Machine Learning for Cardiovascular Image Analysis”. This opportunity is available
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focus will be on biomechanics, image processing, machine learning (ML), artificial intelligence (AI), and metrology, the student will also contribute to the co-design of cadaver experiments and data
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image sequences. As a benchmark, end-to-end deep learning models will be developed using raw image data. In parallel, shallow learning models (e.g., Gaussian processes) will be explored based on insights
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Alex Leung (Mechanical Engineering at UCL) will also collaborate. he specialises in imaging of additive manufacturing and will support the project by assisting with the in-process monitoring. We expect
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/biomedical engineering or of relevant scientific field A solid background in machine learning Extensive experience with either computer vision or image analysis Good knowledge of deep learning packages
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multidisciplinary hub for engineering science and technology research, driving the development of innovative, sustainable and smart processes and products through world-class research while actively investing in
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Your Job: At the Electrocatalysis department of Prof. Karl Mayrhofer, we offer a PhD position within the team Nanoanalysis of Electrochemical Processes. Lead by Dr. Andreas Hutzler, the team is
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-precision metrology instruments Wafer bonding in semiconductor manufacturing Engine docking assemblies Medical diagnostic and imaging systems A key aspect of this research is addressing fundamental
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reputation for carefully designed and precisely controlled experiments, high quality temporally and spatially resolved field experiments using particle image velocimetry combined with synchronised measurements
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Profile: University degree (M.Sc., diploma or equivalent) in materials science, engineering sciences, or physics Experience in at least one of the methods of X-ray imaging, transmission electron microscopy