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challenges. We choose to build Swinburne as the prototype of a new and different university – one that is truly of Technology, of Innovation and of Entrepreneurship. We are committed to a differentiated
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of electron microscopy imaging and spectroscopy to reveal the structure–property relationships that govern molecular adsorption mechanisms. This interdisciplinary project is fully funded by DTU’s PhD grant
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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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Applicants should hold a relevant MSc degree in electronics, electrical engineering, computer engineering, or related fields. Required Qualification: Solid background in digital CMOS design and deep learning
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highly motivated Doctoral Research Fellow in a full time (100 %) fixed term position available for three (3) years at the Faculty of Computer Science, Engineering and Economics, Department
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Swinburne’s strategy draws upon our understanding of future challenges. We choose to build Swinburne as the prototype of a new and different university – one that is truly of Technology, of Innovation and of
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including the newly established Quantum Hub in Sensing, Imaging and Timing (QuSIT) and a newly awarded Royal Academy of Engineering (RAEng) Research Chair on multistatic radar systems. Finally, it will
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assaulting them. Second, technology has contributed to the creation of new forms of sexual harm, such as image-based abuse and deepfake technology. The implications of these developments are twofold. First
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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
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per year for 3.5 years. Lead Supervisor’s full name & email address Dr Massimiliano Fasi: m.fasi@leeds.ac.uk Project summary The growing importance of artificial intelligence is fostering a paradigm