8 of 8 Bayesian Methods Jobs in the UK excluding London

Python skills; familiarity with R for MMM. Expertise in regression modeling, statistical and ML techniques . Experience with probabilistic programming, Bayesian methods, and MCMC. Proficient in SQL and/or Spark for large-scale data mining. Solid understanding of statistical foundations and mathematical modelling. Familiarity with More ❯

junior team members, leading research projects, and guiding collaborative efforts is a plus. Knowledge of Machine Learning Domains: Generative models leveraging diffusion or Bayesian Flow Networks. Modelling multimodal data. Large-scale distributed machine learning training. Knowledge, Experience, or Interest in Biological Domains: Drug discovery and protein engineering. More ❯

modelling, machine learning, and probability theory, preferably in the sports or gaming/betting industries. Familiarity with techniques such as Monte Carlo simulation, Bayesian modelling, mixed effects models, Kalman filters, GLMs, and time series forecasting. Strong programming skills, particularly in Python. Experience in exploring new datasets, identifying More ❯

specialist systems, game theory, decision support systems, multi-agent systems Data fusion and state estimation/tracking algorithms e.g. Kalman Filtering, multiple-model tracking methods, particle filters, grid-based estimation methods, Multi-Object-Multi-Sensor Fusion, data-association, random finite sets, Bayesian belief networks, Dempster … Shafer theory of evidence Machine Learning for regression and pattern recognition/discovery problems e.g. Gaussian processes, latent variable methods, support vector machines, probabilistic/statistical models, neural networks, Bayesian inference, random-forests, novelty detection, clustering Deep Learning e.g. Deep reinforcement learning, Monte-Carlo tree search More ❯

and reporting as well as ensuring the availability of clean, structured, and reliable marketing data. Explore and implement advanced modelling techniques such as Bayesian MMM, hierarchical modelling, and causal inference. Present findings and insights to senior stakeholders, communicating complex concepts in a clear and actionable way. Stay … business impact. Demonstrated ability to present findings to both technical and non-technical audiences, with experience leading key technical projects. Preferred: Experience with Bayesian modeling, causal inference, and advanced statistical techniques. Prior experience in B2B marketing analytics. Prior experience in a consulting or marketing agency environment is More ❯

open-ended positions. Applicants are invited from any area of applied statistics, including statistical or actuarial data science. Those working in actuarial science, Bayesian statistics, statistical learning and/or actuarial statistics are particularly welcome to apply. Candidates who would like to work with the University's … field. You will have a strong track record of research in applied statistics, actuarial or statistical data science which includes fields such as Bayesian statistics, statistical learning, actuarial statistics, computational statistics and statistical methodology. You will either be established or have the potential to establish yourself as 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 ❯

talented Quantitative Analyst to join their London based team. The successful candidate will use the extensive datasets to enhance existing predictive models, research new methods, and turn your insights into production-ready solutions. This research will involve a mix of well-executed analyses and innovative modelling to solve unique … challenges in football analytics, where traditional methods often need to be adapted or reinvented. To achieve this, you will have the freedom to explore and develop your own ideas while working collaboratively with a team of quants, developers, and analysts, to combine technical expertise with football knowledge. Key Requirements … predictive modelling, machine learning, and probability theory, preferably in sports or gaming/betting industries Familiarity with techniques such as Monte Carlo simulation, Bayesian modelling, mixed effects models, Kalman filters, GLMs, and time series forecasting. While expertise in every area isn’t expected, you should have a More ❯