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Control” (DP250101137). The Postdoctoral Research Fellow is expected to undertake research on control theory and machine learning techniques for project DP250101137. The Postdoctoral Research Fellow will
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is currently seeking a Postdoctoral Research Associate in the development and application of hybrid numerical-asymptotic methods to nonlinear systems. Your key responsibilities will be to: work on an
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interdisciplinary research, bridging gaps across various fields to stimulate innovation and enhance the relevance of their work. The School is seeking expressions of interest from qualified individuals for research
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(OB), Organization Theory (OT), and Strategy, Innovation, & Entrepreneurship (SIE). This role will complete a range of functions which may include: Perform data entry. Format articles. Collect research
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engage in interdisciplinary research, bridging gaps across various fields to stimulate innovation and enhance the relevance of their work. The School is seeking expressions of interest from qualified
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optimisation.” The successful candidate will be involved in developing new mathematical theory and designing and implementing optimisation algorithms relevant to quantum information. The Research Fellow will
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their best work. The School of Chemistry is internationally recognised for its excellence in research and teaching. Our mission is to nurture students to become leaders who will generate societal, economic
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Australian National University | Canberra, Australian Capital Territory | Australia | about 18 hours ago
: Bayesian Machine Learning – Led by Dr Thang Bui, this project focuses on sequential decision-making and bridging deep learning theory and practice. Applicants with expertise in probabilistic modelling
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essential, mathematical theory frequently suggests its value is low. This misalignment creates a gap between modelling and practice. The research will focus on developing new theory to explain why
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 5 days ago
deep learning theory and practice. Applicants with expertise in probabilistic modelling, approximate inference, deep learning, or Bayesian optimisation are encouraged to apply. Interpretable Machine