26 to 46 of 46 Bayesian Methods Jobs in the UK

probabilistic representations for diverse ML tasks. Build and optimize tools for scalable probabilistic inference under memory, latency, and compute constraints. Apply and innovate on methods like Bayesian neural networks , variational autoencoders , diffusion models , and Gaussian processes for modern AI use cases. Collaborate closely with product, engineering … uncertainty quantification, and responsible AI. Requirements/Qualifications: Strong experience in Bayesian inference and probabilistic modeling : PGMs, HMMs, GPs, MCMC, variational methods, EM algorithms, etc. Proficiency in Python (must) and familiarity with PyMC, NumPyro, TensorFlow Probability , or similar probabilistic programming tools. Hands-on experience with classical … ML and modern techniques, including deep learning , transformers , diffusion models , and ensemble methods . Solid understanding of feature engineering, dimensionality reduction, model construction, validation, and calibration. Experience with uncertainty quantification and performance estimation (e.g., cross-validation, bootstrapping, Bayesian credible intervals). Familiarity with database and data 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 ❯

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 ❯

narratives that influence decisions, combining a deep understanding of business priorities with impactful communication. Forecasting & Statistical Knowledge : Proficient in statistical methodologies (e.g., Frequentist, Bayesian) and their application to forecasting, capacity planning, and experimentation pipelines. Enablement & Advocacy : Passionate about empowering teams through self-service analytics, training, and fostering 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 ❯

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 ❯

to grow and you to mentor. What you bring to the table Enthusiasm about using machine learning, especially deep learning and/or probabilistic methods, for science and engineering. Ability to scope and effectively deliver projects. Strong problem-solving skills and the ability to analyse issues, identify causes, and … applied statistics, mathematics, physics, engineering, or a related field, with particular expertise in any of the following: operator learning (neural operators), or other probabilistic methods for PDEs; geometric deep learning or other 3D computer vision methods for point-cloud or mesh-structured data; generative models for geometry and … spatiotemporal data (VAEs, Diffusion Models, Bayesian non-parametric, scaling to large datasets, etc.). Ideally, >2 years of experience in a data-driven role, with exposure to: building machine learning models and pipelines in Python, using common libraries and frameworks (e.g., NumPy, SciPy, Pandas, PyTorch, JAX), especially More ❯

Experience, Skills and Education you will need to have to qualify for this role : Knowledge and Experience; Strong basis in fundamental statistical concepts and methods and familiarity with techniques such as development of predictive equations, survival analysis (including parametric methods), longitudinal data analysis, meta-analysis, mixed treatment comparison … and other hierarchical analysis techniques. Familiarity with machine learning techniques and Bayesian statistics is a plus. Strong statistical programming skills with standard software, including SAS, R, or STATA. Strong communication (spoken and written) and problem-solving skills, and an ability to learn quickly. Ability to communicate effectively … not limited to: Supervised and unsupervised learning Variations in machine learning algorithm development such as regression, classification, clustering, and dimensionality reduction Variations of ensemble methods such as boosting, bagging, and stacking to improve model performance Deep learning Super learners Targeted learning Target maximum likelihood estimation Target trial emulation and More ❯

team will involve growing and demonstrating your skills in several key areas, including but not limited to: Model development: Explore and integrate innovative modelling methods into our training pipeline to enhance the predictive power and flexibility of our model. Take responsibility for the full lifecycle of the model, including … building machine learning models from scratch (e.g., built your own optimiser). Excellent knowledge of stochastic processes and related mathematical techniques. Experience with Bayesian analysis. Experience with Python. (Note: we mostly work in Python.) Knowledge of financial concepts (e.g. calculations with deterministic cash flows). The salary 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 ❯

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 ❯

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 ❯

particularly for trackers and long-running client projects. Independently running end-to-end research and insight projects (primarily quant but also qual and mixed methods). Contributing to and owning RFPs and other proposals. Who you are (skills and experience) 3+ years analytics and/or research experience using … in R or Python for data analysis and data visualisation. Nice to have The ideal candidate has experience in using both frequentist and Bayesian approaches. Understanding of the full market research project life cycle. Focaldata is an equal opportunities employer. We believe in the value of a 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 ❯

that drive the ToffeeX engineering design software. You will collaborate with a multidisciplinary team to solve complex engineering problems, focusing on mathematical optimization, numerical methods for partial differential equations (PDEs), and computational geometry. This role offers the opportunity to work at the intersection of advanced mathematics, engineering, and technology … solve unique real-world engineering problems. Essential Skills & Experience Ph.D. in Applied Mathematics, Physics, Engineering, or a closely related field. Research level experiencein numerical methods for PDEs, mathematical modelling or PDE-constrained optimization. Hands-on experience in developing and implementing numerical algorithms for solving complex simulation or optimization problems. … numerical optimization topics such as nonconvex, robust, or Bayesian optimization. Experience with topology or shape optimization. Experience with AI/ML methods for physics or optimization problems. Experience with software development in C++, Python, or similar languages. Company Benefits Flexibility and support for continuous learning and 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 ❯

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 ❯

About The Role As a Quantitative Analyst on our prediction team, you will use our 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. You will … 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 ❯

best practices for testing exercises. Developing our approach to uncertainty quantification and significance testing, increasing statistical power (given time and token constraints). Developing methods for inferring model capabilities across given domains from task or benchmark success rates, and assigning confidence levels to claims about capabilities. Predictive Evaluations: The … of machine learning, and statistical analysis (T-shaped: some deep knowledge, lots of shallow knowledge, in e.g. experimental design, A/B testing, Bayesian inference, model selection, hypothesis testing, significance testing). Deeply care about methodological and statistical rigor, balanced with pragmatism, and willingness to get into More ❯