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Field
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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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/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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-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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to research this topic. Interest in laboratory work and basic technical understanding. Fluent written and spoken English. Programming skills in e.g. Python, R, Matlab and Java. Experience in image processing
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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
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At the Faculty of Engineering and Science, Department of Materials and Production a position as PhD stipend in Muscle Neuromechanics and Ultrasound Imaging, within the doctoral programme Materials
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Qualifications Master or Honours About Swinburne University of Technology Swinburne’s strategy draws upon our understanding of future challenges. We choose to build Swinburne as the prototype of a new and
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Understanding (Prof. Dr. Martin Weigert) Research areas: Machine Learning, Computer Vision, Image Analysis Tasks: fundamental or applied research in at least one of the following areas: machine learning
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– Imaging Technology Design Research Monash University Design Health Collab, nyein.aung1@monash.edu Applications Close: Thursday 31 July 2025, 11:55pm AEST Supporting a diverse workforce