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entitled “Beyond Data-Augmentation: Advancing Bayesian Inference for Stochastic Disease Transmission Models”. The overarching aim of the project is to develop the next generation of statistical tools
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in chemistry and biology, approaches for extracting relevant information from foundation models, and/or methods for adaptive experimental design such as active learning or Bayesian optimization
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Ability to analyse and visualise disease surveillance and population health data; knowledge of infectious disease transmission dynamics; and competence in applied Bayesian statistical modelling. Strong
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) incorporation of expert knowledge in model building through Bayesian prior elicitation, and 3) develop new methods for identification of conflicts in different parts of complex models. BioM is an
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getting Bayesian type uncertainty for parameters given data (i.e., a posterior type distribution over the parameter space) without specifying a model nor a prior. Such methods can in principle be applied
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Elhoseiny, Code: https://github.com/yli1/CLCL Uncertainty-guided Continual Learning with Bayesian Neural Networks (ICLR’20), Sayna Ebrahimi, Mohamed Elhoseiny, Trevor Darrell, Marcus Rohrbach, Code: https
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2028 working 21 hours per week. We are looking for a Senior Research Fellow in Statistics to advance the dynamic research portfolio of the Population Data Science group (https://popdatasci.swan.ac.uk
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, Artificial Intelligence , Bayesian Statistics , Big Data , Scientific Machine Learning , Social Sciences , Biomedical Informatics , Causal Inference , Computational Social Science , Data Science and
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second in the UK for research power and first in England. The UCL Hawkes Institute (https://www.ucl.ac.uk/hawkes-institute/ ) combines methodological researchers from the Departments of CS and Medical
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getting Bayesian type uncertainty for parameters given data (i.e., a posterior type distribution over the parameter space) without specifying a model nor a prior. Such methods can in principle be applied