126 to 150 of 174 Bayesian Methods Jobs in the UK

data structures using SQL or Python for data analysis. Statistical mindset with the ability to setup, run and analyse RCTs and split tests. Extra bonus for knowledge of Bayesian Statistics. Experience optimising user journeys and driving customer engagement. Passionate about helping small businesses thrive-belief in our mission is important! The salary We expect to pay More ❯

with front-end development: HTML; CSS; React; Javascript/Typescript. Experience with UI/UX design principles. Experience with PyTorch or other deep-learning libraries. An understanding of Bayesian statistics. Location: This role is based on-site at digiLab's offices on the Quay, Exeter. Our Culture and Values: At digiLab, we prioritize work-life balance with More ❯

United Kingdom 6 days ago London, England, United Kingdom 1 week ago London, England, United Kingdom 1 day ago London, England, United Kingdom 3 weeks ago Research Engineer - Bayesian Optimisation (Contractor) London, England, United Kingdom 1 year ago London, England, United Kingdom 1 week ago London, England, United Kingdom 1 week ago London, England, United Kingdom More ❯

start from October 2022, or sooner. The Statistics Section in the Department of Mathematics is committed to research in Statistical Science, broadly interpreted. We undertake rigorous interdisciplinary research, developing methods for applied problems, and the statistical theory underpinning these methods. Expertise in statistical learning, Bayesian, computational, time series, spatio-temporal, and applied-probabilistic methods is More ❯

in their own abilities, be happy to work alone and as part of a small team and have excellent communication and problem-solving skills. Familiarity and confidence in Bayesian statistics and simulation techniques, including experience of programming in Python or R, is essential. The department has gained a Silver Athena SWAN award as a commitment to providing More ❯

Data Science. These are both 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 multi-disciplinary Global Research Institutes … internationally excellent research in your 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 an international research leader, with More ❯

new geographies. Lead robust model evaluation and verification work to evaluate the model outputs and document models for internal and external communication for banking clients. Disseminate scientific insights and methods to engage with stakeholders, customers, the scientific community - representing the team at international conferences and industry events. Collaborate across scientific and technical domains through working with other climate and … in the role we encourage you to apply! Experience researching (extra) tropical cyclone impacts and risk. Experience working with catastrophe models, particularly for wind based perils. Experience with Bayesian statistics and/or Extreme Value Theory for extreme events Qualifications PhD or equivalent experience in climate science, hazard modeling or statistical analysis. Equal Opportunities Climate X are More ❯

approaches when the task calls for it Values communication, empathy, and teamwork Real-world industry experience is strongly preferred, as we're looking for candidates who can apply advanced methods to practical, high-impact problems and navigate the complexity of real operational environments. We welcome applications from both mid-level and senior professionals; however, due to the complexity of … chance to learn and work with: Physical and empirical modelling of complex systems Machine learning for prediction and inference Optimisation and real-time decision engines Bayesian methods and uncertainty quantification Collaborative software development and production deployment What We Offer A truly inclusive, supportive team culture Flexible working hours and remote-first environment Competitive salary with performance More ❯

approaches when the task calls for it Values communication, empathy, and teamwork Real-world industry experience is strongly preferred, as we're looking for candidates who can apply advanced methods to practical, high-impact problems and navigate the complexity of real operational environments. We welcome applications from both mid-level and senior professionals; however, due to the complexity of … chance to learn and work with: Physical and empirical modelling of complex systems Machine learning for prediction and inference Optimisation and real-time decision engines Bayesian methods and uncertainty quantification Collaborative software development and production deployment What We Offer A truly inclusive, supportive team culture Flexible working hours and remote-first environment Competitive salary with performance More ❯

A high level of mathematical competence. The ability to code or have programming experience, especially in Python. Some experience with theoretical concepts of statistical learning (e.g. hypothesis testing, Bayesian Inference, Regression, SVM, Random Forests, Neural Networks, Natural Language Processing, optimisation). Experience with some coding libraries frequently used in data science. The ability to communicate effectively. Experience More ❯

Particle Filter). Sensor calibration. Background in linear algebra and probability/stochastic processes. Familiarity with non-linear optimisation frameworks like Ceres, g2o, GTSAM, etc. Probabilistic inference and Bayesian likelihood estimation (e.g., Markov Chain Monte Carlo). The salary range for this role is an estimate based on a wide range of compensation factors, inclusive of base More ❯

Theo Damoulas and Prof. Mark Steel, as part of the Turing-Lloyds Register Foundation funded project 'Air Quality Sensor Networks'. This project is likely to involve hierarchical Bayesian models, nonparametric Bayesian inference, graphical models, active learning, experimental design and issues in spatio-temporal inference such as non-stationarity and non-separability. The expectation … or Computer Science or Applied Mathematics (or you will shortly be obtaining it). You should have a strong background in one or more of the following areas: Bayesian inference, spatial statistics, probabilistic machine learning. The post is based in the Departments of Statistics and Computer Science (joint appointment) at the University of Warwick, but the work More ❯

Theo Damoulas and Prof. Mark Steel, as part of the Turing-Lloyds Register Foundation funded project ‘Air Quality Sensor Networks’. This project is likely to involve hierarchical Bayesian models, nonparametric Bayesian inference, graphical models, active learning, experimental design and issues in spatiotemporal inference such as non-stationarity and non-separability. The expectation is … or Computer Science or Applied Mathematics (or you will shortly be obtaining it). You should have a strong background in one or more of the following areas: Bayesian inference, spatial statistics, probabilistic machine learning. If you have not yet been awarded your PhD but are near submission or have recently submitted your PhD, any offers of More ❯

and implementing advanced mathematical models 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 in a rapidly growing company. … literature, developing creative approaches to 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. You thrive in a fast … for physical systems. Experience in 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 professional growth. 25 days holiday More ❯

development of multiple proprietary betting 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 in any programming language, but More ❯

development of multiple proprietary betting 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 in any programming language, but More ❯

Physics, or similar). Experience building and maintaining predictive credit models. Fluency in Python (Pandas, NumPy, SciPy, Matplotlib) and SQL. Experience with advanced modelling techniques (e.g., Monte Carlo, Bayesian modelling) is a plus. Strong communicator. Commercial mindset and strong instincts around risk and return. Experience mentoring or managing analysts. Knowledge of the German lending market or language More ❯

Physics, or similar). Experience building and maintaining predictive credit models. Fluency in Python (Pandas, NumPy, SciPy, Matplotlib) and SQL. Experience with advanced modelling techniques (e.g., Monte Carlo, Bayesian modelling) is a plus. Strong communicator. Commercial mindset and strong instincts around risk and return. Experience mentoring or managing analysts. Knowledge of the German lending market or language More ❯

Physics, or similar). Experience building and maintaining predictive credit models. Fluency in Python (Pandas, NumPy, SciPy, Matplotlib) and SQL. Experience with advanced modelling techniques (e.g., Monte Carlo, Bayesian modelling) is a plus. Strong communicator. Commercial mindset and strong instincts around risk and return. Experience mentoring or managing analysts. Knowledge of the German lending market or language More ❯

architecture to capture subtle market signals. They will rely on your deep knowledge of deep learning, whether your background is in LLMs, recsys, image models, RL agents, or classical methods, to help shape the next generation of their ML-driven trading. You’ll also contribute to hiring, mentor teammates, and share insights from the broader research community through papers … experience in industry applying ML to challenging problems Expertise in one or more of: deep learning, reinforcement learning, non-convex optimisation, approximate inference, NLP, or Bayesian methods Strong programming skills, ideally in Python, with experience using tools like NumPy, Pandas, JAX, PyTorch or TensorFlow A strong publication record in top-tier venues (e.g., NeurIPS, ICML, ICLR More ❯

architecture to capture subtle market signals. They will rely on your deep knowledge of deep learning, whether your background is in LLMs, recsys, image models, RL agents, or classical methods, to help shape the next generation of their ML-driven trading. You’ll also contribute to hiring, mentor teammates, and share insights from the broader research community through papers … experience in industry applying ML to challenging problems Expertise in one or more of: deep learning, reinforcement learning, non-convex optimisation, approximate inference, NLP, or Bayesian methods Strong programming skills, ideally in Python, with experience using tools like NumPy, Pandas, JAX, PyTorch or TensorFlow A strong publication record in top-tier venues (e.g., NeurIPS, ICML, ICLR More ❯

on this job and more exclusive features. The Quant team at Mustard Systems create predictive models that are globally recognised as best-in-class. We use cutting-edge statistical methods to model a wide variety of sports, often developing our own techniques. We're looking for talented individuals to join our team. You can expect: To research recent advances … in Bayesian updating to better extract signals from noisy data To join a close-knit, collaborative team of mathematicians, and enjoy a high level of responsibility from an early stage To design and build your own models using your own ideas To work closely with expert traders and developers to improve your models Requirements What we're More ❯

firm with startup-style freedom to build sports models. Role Overview Develop American football, baseball, tennis & ice hockey models & more Price same-game parlays using advanced stats Apply Bayesian inference, filtering & mathematical modeling Work with quant engineers on dashboards & implementation Ideal Candidate Strong quant background (Bachelor’s/Master’s; PhD a plus) Experience in syndicate or More ❯

firm with startup-style freedom to build sports models. Role Overview Develop American football, baseball, tennis & ice hockey models & more Price same-game parlays using advanced stats Apply Bayesian inference, filtering & mathematical modeling Work with quant engineers on dashboards & implementation Ideal Candidate Strong quant background (Bachelor’s/Master’s; PhD a plus) Experience in syndicate or More ❯

for Doctoral Training in Mathematics for Our Future Climate and has an open PhD position at the intersection of data assimilation and machine learning. Machine Learning Approaches in Bayesian and Ensemble Data Assimilation Probabilistic data assimilation (DA) is the process of combining models with observations to obtain the filtering distribution—the conditional probability over states given past … will focus on learning ensemble DA algorithms for use in high-dimensional chaotic systems such as the atmosphere. Initial application will be to idealised problems, but scaling up these methods to operational weather prediction will also be explored. Theoretical issues about learnability and comparisons to other methods will also be considered. The combination of ML with DA is … an active and quickly expanding area of research. However, the learning DA algorithms is an underexplored field and has the potential to significantly improve on current DA methods used for weather and climate forecasting. The student would thus be at the frontier of high-impact DA research, working with world-leading institutions on research in DA (Reading), Earth observation More ❯