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, attention-based models, and multi-modal learning approaches—to model RNA-mediated regulatory mechanisms and their dynamic interactions in disease. The position is part of an NHMRC Ideas Grant project
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networks and/or probabilistic graphical models; and causal inference. An outstanding publication record in top tier machine learning and/or computer vision conferences or journals, commensurate with
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: Developing and deploying machine learning models (e.g. graph neural networks, neural force fields, diffusion models) for molecular property prediction and molecular generation. Integrating quantum chemistry
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within a ‘Group of Eight’ University! The State Government of South Australia, CSIRO and the University of Adelaide’s Australian Institute for Machine Learning (AIML) are actively interested in advancing
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: 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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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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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | about 23 hours 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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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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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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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