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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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and learned surrogates with clear statistical validation; Bayesian inverse problems and data assimilation via measure transport and amortized inference; robustness and distribution shift in scientific
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the virtual embryo; and Bayesian Inference to calibrate model parameters and identify developmental control points. You will also gain experience in the simulation-experiment loop, where computational
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foundations, combining ultrasonic guided wave monitoring, high-fidelity finite element simulations, Bayesian inference, and machine learning. Guided waves can propagate over long distances and reach areas
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) Uncertainty quantification, 4) Model interpretability. Experience with other deep learning methods, such as Convolutional or Bayesian Neural Networks, Simulation-Based Inference (SBI), Normalizing Flows
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Bayesian ML approaches for path inference; introducing sensors; behaviour classification; resource-constrained active-learning; other IoT applications; microbattery development and field experiments and
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silico model of normal development. Bayesian inference will calibrate model parameters and highlight control points, with predictive accuracy benchmarked against existing perturbation datasets. O3. Map
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principles and analytic methods relevant in health services research Advanced knowledge of statistical computing and/or Bayesian inference Advanced programming skills in a common statistical software package
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inference, analysis of high-dimensional and -omics data, Bayesian methods, and clinical trials, with active collaborations in cancer, aging, HIV, and the analysis of large-scale health data. The School
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of statistical analyses, in particular: Exploratory and confirmatory factor analysis, Multilevel analyses (including latent class analysis), Time series analysis, Bayesian inference methods, Regression techniques