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to generate data which can be coupled with robust and physics-informed machine learning and reduced-order modelling methods for model predictive control and reinforcement learning. PhD (or soon to be completed
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simulations using DFT (particularly of surface processes); kinetic Monte Carlo simulations; molecular dynamics simulations; classical and machine-learned force fields. Highly developed skills in scientific
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Integrated Planning & Learning and Reinforcement Learning in non-deterministic and partially-observed scenarios. The methods will be evaluated on physical robots. The ideal candidate would have: A PhD (or
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Australian National University | Canberra, Australian Capital Territory | Australia | about 2 months ago
for uncertainty quantification in learned computer vision. The person should have a PhD in Computer Vision or a closely related field, and a demonstrated strong track record in this field. This should include
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training materials for research teams, focusing on data science and machine learning techniques in geoscience. Position description: PD [Research Fellow] [520112].pdf To learn more about this opportunity
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), clinical trials, disease surveillance, and the use of novel methods including Bayesian network, machine learning, social network analysis and dynamic data visualisation tools. Further information is
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area of expertise. You may be a great fit if: You are a passionate researcher with a PhD in Computer Science or a related field, experienced in machine learning for spatial data management, with a track
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splitting and C–N coupling reactions. Work includes computational modeling of carbon-based materials, conducting simulations to understand reaction mechanisms, and developing and applying machine learning
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and standing recognised by the University/profession as appropriate for the relevant discipline area (e.g., AI/Machine Learning, Bioinformatics). A proven track record of research and scholarly
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research activities, and engaging in teaching, student supervision, and mentoring in the areas of edge computing and machine learning. About You The successful candidate will hold a PhD in Computer