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very high resolution, suitable for detecting photovoltaic modules and the cleanliness of solar panels. These images and other data can be processed by computer vision and machine learning methods
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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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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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decision-making Prototype and test digital tools in a real-world practice environment Contribute to both immediate practical improvements and longer-term strategic visions for architectural practice
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structural health monitoring, especially on computer vision, image processing, machine learning, deep learning, signal processing and data analysis techniques, are preferred. Application process To apply
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confirmation) The Opportunity This is an opportunity for a talented PhD candidate with strong skills in developing prototype systems for Extended Reality (XR - which encompasses augmented and virtual reality
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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 different university – one that is truly
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. Objective 2: Toolkit Development. The student will work with our consumer groups and the design team to develop the toolkit. They will develop a prototype and run a series (n=3) of elevation sessions with
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are seeking a highly motivated and enthusiastic candidate with a strong interest in computer vision, AI, and robotics. The ideal candidate will have solid programming skills, particularly in Python, and be well
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outcomes that make significant contributions to improving the lives of others globally. Our overarching vision is to make a significant and long-lasting impact that changes the world around us, beginning in