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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
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deep learning, imaging and data analysis would be helpful for this project. Must be eligible to enrol in PhD programs at Curtin University. Application process Please send your CV, academic transcripts
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drones can capture visual and thermal imaging at a very high resolution, suitable for detecting photovoltaic modules and the cleanliness of solar panels. These images and other data can be processed by
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your own learning at one of the world’s top 80 universities Take your career in exciting, rewarding directions Fibrosis is a scarring process characterised by the excessive deposition of connective
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isothermal flow conditions relevant to bubble reactors using optical diagnostic methods, followed by image processing, which may include machine learning-based techniques. To analyse bubble dynamics from
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PhD Scholarship in Integrated Photonics for Telecommunication, Biosensing and Precision Measurements
propagation Interfacing to array microfluidics Image analysis of biosensor response Sensor surface biofunctionalisation Optical communications High-speed signal analysis Modelling of optical propagation in
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in the below key accountabilities: Experience with training and implementing neural networks and/or analysing satellite imagery Experience working with large databases and/or developing image
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/ML technologies can transform architectural workflows and creative processes. This distinctive arrangement offers both immediate practical insights and the opportunity to envision longer-term
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our understanding of future challenges. With this new strategic plan, we choose to build Swinburne as the prototype of a new and different university – one that is truly of Technology, of Innovation and
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publications and research experiences in structural dynamics and structural health monitoring, especially on computer vision, image processing, machine learning, deep learning, signal processing and data