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-sensor hyperspectral data for crop disease detection and monitoring. Develop machine learning or physics-informed models to retrieve reliable spectral and/or biophysical features, from multi-scale
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, ticketing tools, web chat etc). Demonstrated ability in the use of a range of computer applications (Windows) and the ability to quickly learn new technologies. Desirable but not essential – Experience with
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societal applications: Computer Systems and Computing Infrastructures Cybersecurity and Trustworthy Digital Systems Machine Learning and Artificial Intelligence Robotics and Autonomous Systems All positions
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based at other campuses of the University About the role As Assistive Technology Specialist, you will play a key role in supporting equitable access to learning and research by providing expert guidance
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development. Familiarity with software development lifecycle including design and documentation of software architecture, testing/debugging skills, and version control. Experience applying machine learning
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materials systems at the molecular level with machine learning. The PhD Student will undertake a study analysing mass spectral imaging data streams in real time using machine learning workflows. A pathway for
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student societies, and are actively involved in teaching and learning opportunities within the school. For further information about the School, please visit - https://www.unsw.edu.au/engineering/our
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Science and Machine Learning Cybersecurity Software Engineering Digital Health Computer Science Education To Apply Please submit your CV and covering letter addressing your suitability for this position
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premium student experience and to developing leaders for society. The School’s vision is to be the place of choice for tomorrow’s business leaders to study and learn their craft. It offers a range of
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: ability to use tools such as R (or similar platforms) for data wrangling, visualisation, and reproducible workflows. A working knowledge of statistics and applied machine learning methods (e.g. regression