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aims to define the optimal specifications for spaceborne hyperspectral sensors to detect diseases in Australia’s key wheat and barley crops. The successful candidate will lead the development and
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borehole seismic data obtained with various sensors. Assist in planning, design and implementation of field experiments to collect high quality geophysical data for research projects utilising fibre optic
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project with real clinical potential The Opportunity Are you a highly motivated and technically proficient researcher with expertise in electrochemical sensor development and biomedical engineering? We
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or condensed matter physics, with a willingness to engage in experimental applications. The successful applicant will also be involved in the design, fabrication and measurement of quantum sensors in the Jesper
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systems for sensor alignment, motion control, and structural stability; collaborating on the assembly, integration, and optimisation of laser imaging platforms; conducting testing and validation to ensure
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working with industry partners to translate this new technology into commercial sensors for vineyard installation. Further opportunities involve integrating the research data into the National Smoke
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Professor Kalina Yacef. These projects explore how multimodal data from online platforms and sensor-based environments can be harnessed to enhance learning at scale. Research directions include designing
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to an exciting research project on social network analysis and online influence. This position will collaborate with industry partners and academics within the Adelaide Data Science Centre. To be successful you
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Apply now Job no:501425 Work type:Part time Location:Hobart Categories:Research Focused Engage in collaborative research as part of the EarthBank project of the AuScope Geochemistry Network
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Australian National University | Canberra, Australian Capital Territory | Australia | about 1 month ago
at the John Curtin School of Medical Research (JCSMR), within the ANU College of Science and Medicine. This role will lead the development of advanced deep learning frameworks—including graph neural networks