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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
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, including with the use of machine learning use this knowledge to design excavation trajectories that reduce energy consumption develop mathematical models of excavation energy losses communicate your findings
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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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learning at scale. Research directions include designing algorithms and methods for adaptive and personalised feedback, modelling learning behaviours with sequence and deep learning methods, and generating
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-based algorithms (e.g., GNNs, deep reinforcement learning) design and simulate dynamic models of megaproject systems prepare and submit journal articles to high-impact publications contribute
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individuality is welcomed and celebrated To learn more about the School of Geosciences, click here About you The University of Sydney values courage and creativity; openness and engagement; inclusion and
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, improving equity, access and opportunity, and fostering an environment where individuality is welcomed and celebrated To learn more about the School of Geosciences, click here About you The University
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for their outstanding commitment to teaching excellence. To learn more about the School of Chemistry, click here About you The University values courage and creativity; openness and engagement; inclusion and diversity
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disciplines including aerospace, combustion, design, fluid mechanics, materials, mechanical, mechatronic and robotics engineering. To learn more about the School click here . The Clean Combustion Group