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to statistical computing, Bayesian modeling, causal inference, clinical trials and analysis of complex large-scale data such as omics data, wearable tech, and electronic health record, with specific preference
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be inferred from models that are incomplete and data that involve errors. For such challenges, Bayesian analysis using Markov Chain Monte Carlo (MCMC) has become the gold standard. For addressing high
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hierarchical Bayesian models to cognitive processes Supervisor: Dr Martin Lages Project aims: New variants of hierarchical Bayesian models of cognitive processes can be investigated using simulated, existing and
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, Bayesian statistics, risk prediction, geospatial analyses, or machine learning. The faculty will be a critical force for planning, funding, promoting, and executing programs for the Department and Institute
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modern clinical trial design, such as Bayesian Adaptive Clinical trial design or established expertise in statistical methods such as structural equation modeling, causal data analysis. Experience in
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guiding materials measurement experiments to acclerate learning the synthesis-process-structure-property relationship. Machine learning methods include, but are not limited to, Bayesian inference
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to academic leadership. Renowned for his work in Big Data and healthcare innovation, Dr. Madigan has authored over 200 publications covering topics such as Bayesian statistics, text mining, and probabilistic
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using Bayesian approaches vector biology, ecology and control life-history trade-offs and the sustainable control of malaria vaccine development quantitative approaches to vaccine selection host-parasite
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This PhD project is funded by a successful ARC Discovery Project grant: "Improving human reasoning with causal Bayesian networks: a user-centric, multimodal, interactive approach" and the successful
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are required. Advanced competence in multiple approaches, such as neural ODEs, LLMs, bayesian, Lasso, large language models, and ensembling methods is required Experimental Immunologist Research Scientist