30 condition-monitoring-machine-learning Postdoctoral positions at University of Sydney
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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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ternary neural networks using FPGA devices. The successful candidate will have significant experience in machine learning, FPGA design and an outstanding track record in conducting machine learning research
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density) influence energy dissipation develop mathematical models to predict and explain these effects collect and analyse data, including with the use of machine learning use this knowledge to design
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/AI expertise) systems Engineering or Control Systems (with applications to large-scale projects) artificial Intelligence / Machine Learning (with interest in applications to megaprojects or governance
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Postdoctoral Research Associate in Global Environment Modelling of Soil Organic and Inorganic Carbon
. The project is aimed to improve our in-house developed process-based computer model and use it to represent the soil ecohydrological and biogeochemical interactions across various carbon and nitrogen soil pools
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: developing and testing new approaches to water resources modelling, application of Bayesian inference methods to environmental problems, machine learning and data science applications, undertaking analysis and
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of scientific monitoring programs demonstrated experience managing and integrating large datasets experience working in large teams and organising complex field programs a strong commitment to delivering outcomes
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, and we are continuously working to identify and remove biases and barriers to make our workplace open, supportive and safe for everyone. To learn more about the School of Chemistry, click here About you
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across all themes. This project will advance our understanding of the conditions that led to reef growth, stress, collapse, and recovery during periods of rapid climate and environmental change, spanning
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the group. Learnings in perovskite photovoltaic is have also been actively applied to other optoelectronics, photonic devices, dosimeters, storages and electronic devices. Within the group, you will be given