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with SCION in New Zealand bringing together researchers in robotic perception, machine learning, remote sensing and silviculture to transform and upscale forest phenotyping operations. The role will be
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, economic growth, inequality, and culture. The project draws on large-scale data collection and archival records, and applies a diverse set of methodologies such as text analysis and machine learning
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position, available until December 2027. Flexible work arrangements can be negotiated with the right candidate. Be part of the Australian Institute for Machine Learning – the largest computer vision and
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draws on large-scale data collection and archival records, and applies a diverse set of methodologies such as text analysis and machine learning to assess the impact of colonisation. About you PhD in
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biology, agricultural systems, climate science, or a related discipline. Commitment to developing innovative and effective approaches to teaching and learning in areas related to environmental monitoring
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biology, agricultural systems, climate science, or a related discipline. Commitment to developing innovative and effective approaches to teaching and learning in areas related to environmental monitoring
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biology, agricultural systems, climate science, or a related discipline. Evidence of development of innovative and effective approaches to teaching and learning in areas related to environmental monitoring
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biology, agricultural systems, climate science, or a related discipline. Evidence of development of innovative and effective approaches to teaching and learning in areas related to environmental monitoring
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supporting full-scale field testing to assess long-term durability and performance under real service conditions. The role also includes leading material characterisation, corrosion analysis, service life
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focused on the challenge of accelerating ternary neural networks using FPGA devices. The successful candidate will have significant experience in machine learning, FPGA design and an outstanding track