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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 20 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
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algorithms and methods for adaptive and personalised feedback, modelling learning behaviours with sequence and deep learning methods, and generating interpretable insights through novel analytics and
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Build and test predictive models using machine learning techniques Drive methodological innovation in neurophysiological data analysis Contribute to publications, grant submissions and independent
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the application of AI and machine learning to identify novel therapeutic targets and advance precision medicine approaches. For further information, please click here to view the full appointment
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of innovative computational procedures and methodologies, addressing a national skills shortage and enabling timely progress on a high-impact research initiative in modern econometric modelling. The role provides
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Simulation group to apply classical Molecular Dynamics and Machine Learning approaches for development of a new class of hybrid polyphenol-lipid nanoparticles with tuneable internal structure and exploration
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individual to work on an exciting modelling and simulation project, involving extensively applied work at the intersection of software engineering research and modelling and simulation, with real-world testing
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data, spatial modelling, multivariate statistics and/or machine learning, and relevant coding languages (e.g. R, Python), including a sound understanding of FAIR data principles, data management and
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Engineering, etc.), expertise in cutting-edge AI and machine learning is essential; while structure prediction or materials chemistry experience would be advantageous, it is not a pre-requisite for the role
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Melbourne CBD campus About the Role We are seeking a Postdoctoral Research Fellow to join RMIT's Materials Modelling and Simulation group to apply classical Molecular Dynamics and Machine Learning approaches