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Field
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areas Biomedical applications, social determinants of health or other demographic health areas Spatial microsimulation, spatially weighted regression, combinatorial optimization or Bayesian network
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study examining common elements in decisions across different contexts (risk, uncertainty, time; gains, losses, and mixed domain choices). Applying Bayesian techniques to develop stochastic models
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contribute to the excellence of our academic community. We are looking for a postdoctoral researcher with expertise in Bayesian hierarchical spatio-temporal statistics and measurement error methods for a 3
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for screening purposes and cell-based therapies. We will develop methods for modelling missing not at random (MNAR) observations and quantifying uncertainty using Bayesian methods and deep learning architectures
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Experience developing pipelines and code for gravitational-wave searches and/or parameter estimation Knowledge of advanced Bayesian methods and samplers, machine learning approaches to signal processing
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of models of co-infection (including parameter estimation, model calibration, validation, etc.) and close collaboration with researchers, clinicians, and public health partners. Professor Hollingsworth’s
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forecasting. Familiarity with ensemble methods, Bayesian approaches, and uncertainty estimation. Experience with large-scale or messy real-world data (structured and/or unstructured). Interest in or experience
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descent, random forests, etc.) and deep neural network architectures (ResNet and Transformers). Preferred Qualifications: Knowledge of Approximate, Local, Rényi, Bayesian differential privacy, and other
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at the intersection of systems neuroscience and computational modeling. Our lab is broadly interested in Bayesian inference, perception, multisensory integration, spatial navigation, sensorimotor loops, embodied