2 of 2 Permanent Bayesian Methods Jobs in the Thames Valley

external companies, academia, and internal scientific and data science teams Extensive hands-on coding and actively contribute to platform development Clearly articulate highly technical methods and results to diverse audiences and partners to drive decision-making findings through peer-reviewed publications and/or scientific conferences Qualifications Ph.D. in … Proficiency in one or more AI frameworks such as PyTorch or Tensorflow Extensive experience with one or more AI/ML fields including Bayesian Optimization/Bayesian Deep Learning/Probabilistic Machine Learning, Causal Inference and discovery Demonstrated proficiency in working with Foundation Models More ❯

Grenoble IDEX. Application Instructions Interested applicants should write to us with: a letter of interest, CV, and should require two recommendation letters. Context Bayesian deep learning brings together two of the most important machine learning paradigms: Bayesian inference and deep learning. On the one … hand, Bayesian learning provides a theoretically sound framework to formalise the estimation of the architecture and the parameters of deep neural network models. On the other hand, deep learning offers new tools in Bayesian modelling, e.g. to learn flexible nonparametric priors or computationally efficient … few. While very effective, these models are computationally costly and require large quantities of data for their many parameters to be accurately estimated. Bayesian statistics offers a theoretically well-grounded framework to reason about uncertainty, and it is one of the cornerstones of modern machine learning. At More ❯