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postgraduate qualification in one of these fields plus relevant research experience. Demonstrated track record in epidemiological research with outcomes of high quality and high impact with clear evidence of
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, approximate inference, deep learning, or Bayesian optimisation are encouraged to apply. Interpretable Machine Learning for Natural Language – Led by Prof Lexing Xie, this stream applies machine learning
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 3 months 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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engineering and a strong foundation in data science. You bring a passion for solving complex problems and a track record of research excellence in optoelectronic materials, machine learning, or related fields
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the timing, scale, and rate of mammal declines in Australia. They will use critical inferences of past demographic change and high-performance computing to disentangle the ecological mechanisms that were
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to engage with multidisciplinary teams and external partners. Desirable attributes include experience with spatio-temporal models, machine learning, Bayesian methods, and knowledge of environmental exposure
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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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learning and one or more of the following: transformer networks, implicit neural functions, graph neural networks and/or probabilistic graphical models; and causal inference. • An outstanding publication
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immune development technical expertise in flow cytometry, single-cell analysis, microbiome analysis, and large-scale bioinformatics proven track record in nutritional geometry and global data
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opportunities for RCSWA staff and students, track, evaluate and promote student involvement in research and provide reports of student research involvement. About you Masters or PhD in a health-related field, or