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. The ideal candidate will enhance our biostatistical core and complement or deepen our current department strengths, including, but not limited to: Bayesian methods, big data, causal inference, clinical trials
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Australian National University | Canberra, Australian Capital Territory | Australia | about 1 month ago
to: Conduct cutting edge research in machine learning, AI and algorithms, such as but not limited to Bayesian machine learning, human-centered AI and interpretable machine learning, attention markets, gig
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demonstrated publication record in peer-reviewed scientific journals, particularly in avian population ecology Excellent statistical skills, including experience writing Bayesian hierarchical population models
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | about 1 month ago
to Bayesian machine learning, human-centered AI and interpretable machine learning, attention markets, gig economies and prediction markets. Opportunity to supervise research students and work as part of a
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knowledge of key AI methods such as deep learning, operator learning, and Bayesian optimization, and apply it to develop next-generation surrogate models. This position will enable you to coordinate and
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create new mathematical approaches, algorithms and software to advance scientific research in multiple disciplines, often in collaboration with other Flatiron Centers. CCM has particularly strong research
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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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acquired at multiple resolutions (tissue-level, single-cell/nucleus, and spatial transcriptomic data), requiring complex integrative analyses. The successful candidate will lead the analysis of a large
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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