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Field
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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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equilibrium/simulation, surrogate models/ reduced order emulators or Bayesian or interpretable machine learning. Simulation and optimization of on-demand transportation services or novel transit systems and
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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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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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assessment models, and statistical modeling in both frequentist and Bayesian frameworks • A solid track record of publications Appointment Type Restricted Salary Information Commensuate with experience Review
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. Essential Functions/Responsibilities Expertise in all areas of machine learning including deep learning, Bayesian statistics, probabilistic modeling, optimization, learning theory, high dimensional data
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., Estimation of temporal covariances in pathogen dynamics using Bayesian multivariate autoregressive models. PLoS Comput Biol 15, e1007492 (2019). Overview Overview The MRC-University of Glasgow Centre for Virus
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characterization tools for closed loop experiment design, execution, and analysis, where experiment design is guided by active learning, Bayesian optimization, and similar methods. A key challenge is the integration
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ranging from statistical fits to the data to mechanistic descriptions of shock-state evolution and hydrogen transport. Mathematical methods will include multivariate regression and Bayesian parameter