3 of 3 Bayesian Methods Jobs in the South East

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 ❯

sustain the pace to keep on growing? Will you make an impact with your desire to win? Role Overview: The Data Scientist - Commercial (Bayesian Focus) supports the Lead Data Scientist (Commercial) in delivering AI explorations identified via the AI Incubator process. This hands-on role requires expertise … in Bayesian techniques and commercial data science models, such as market mix modelling, to drive impactful business outcomes. Key Responsibilities: Develop and implement commercial data science solutions using Bayesian methodologies. Collaborate with business stakeholders to align AI initiatives with commercial objectives. Build and optimize … and similar solutions to inform decision-making. Provide insights and recommendations based on statistical analysis and machine learning. Stay abreast of advancements in Bayesian modelling and its commercial applications. Qualifications: PhD or Master's degree in Machine Learning, Data Science, or a related field. Strong expertise in 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 ❯