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complete-nutrition crops for on-demand bioresource production. We will test their performance in our physical twin environments and feedback to our machine learning modelling to advance plant growth
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criteria PhD in Computational Biology, Bioinformatics, Computer Science, or a closely related field with a strong focus on machine learning and deep learning applications. Demonstrable experience in
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Biology, Bioinformatics, Computer Science, or a closely related field with a strong focus on machine learning and deep learning applications. Proficiency R, with the ability to handle complex data analysis
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will need: Explicitly address each selection criteria PhD in Computational Biology, Bioinformatics, Computer Science, or a closely related field with a strong focus on machine learning and deep learning
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, proprietary seismic sensors, cutting edge Ambient Noise Tomography, and machine learning to provide the world’s first scalable 3D exploration dataset with near-real-time insights to guide active exploration
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also co-supervise PhD students and Honours students. Outputs will include characterisation of novel genes, improved methods for RG-II analysis, and a better understanding of cell wall assembly and
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enthusiasm for method development and refinement. The postdoc will also co-supervise PhD students and Honours students. Outputs will include methods for successfully growing, fixing and analysing plant
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| 18 Month Fixed Term (with the potential for renewal) The Australian Institute for Machine Learning, located at the University of Adelaide, is at the forefront of artificial intelligence, machine
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development opportunities to its staff, including its own Women’s Professional Development Network to support women to navigate their career. To learn more about The School of Computer and Mathematical Sciences
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physical attributes will support and improve the mapping of seagrasses. · Develop machine learning models and object-based image processing algorithms to differentiate and identify seagrass habitat