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Scalable Inference: Develop new algorithms for scalable uncertainty quantification (UQ) and Bayesian inference and apply them to challenging simulation problems. The goal is to produce robust, validated
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/Seurat, count models, batch correction, differential analyses). Strong grounding in statistics (GLMs, hierarchical/Bayesian modeling, multiple testing) and experimental-design principles. Bioinformatics
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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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-GRASP”, “Simulation-Based Bayesian Inference for Object Perception in Robot Grasping”, financed by the European Union´s Horizon Europe research & innovation programme under the euROBIN project (Grant
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/Seurat, count models, batch correction, differential analyses). Strong grounding in statistics (GLMs, hierarchical/Bayesian modeling, multiple testing) and experimental-design principles. Bioinformatics
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, to support predictive modelling, deep phenotyping, and real-world evidence generation. Apply and refine causal inference methodologies, such as structural equation modelling and Bayesian approaches, to better
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 29 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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(for example, R, Python, or Matlab). Experience with graph modeling, Bayesian statistics, or causal inference is a plus. The candidate will join an integrated team of computational scientists, molecular
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-level models, Bayesian inference, latent class analysis) Strong data visualization skills using packages such as ggplot2, seaborn, or matplotlib Experience with clinical research databases and data
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to implement advanced computational pipelines, including machine learning, deep learning, Bayesian inference, and probabilistic mixed membership modeling for innovative research. · Contribute