6 of 6 Remote 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 ❯

field of machine learning. You will be apart of a highly unique research group across Natural Language Processing, Large Learning Models, deep learning, Bayesian & Signal Optimization with the exciting transition to the world of financial predication through the prism of machine learning. The most exceptional team members More ❯

models. Job Requirements: 2+ years' experience (ideally in a syndicate) developing sports betting models from scratch, in any sport Strong fundamental understanding of Bayesian statistics, variational inference, filtering techniques, state space and hierarchical models MSc+ level degree from well respected university in any STEM subject Excellent proficiency More ❯

models. Job Requirements: 4+ years' experience (ideally in a syndicate) developing sports betting models from scratch, in any sport Strong fundamental understanding of Bayesian statistics, variational inference, filtering techniques, state space and hierarchical models Excellent proficiency in any programming language, but with a preference for Python or 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 ❯