6 of 6 Bayesian Methods Jobs in the Thames Valley

teams to design, implement, and optimize data pipelines and predictive models that inform trading decisions and enhance operational efficiency. RESPONSIBILITIES: Primary Focus: Probabilistic Weather Modelling: Research and prototype probabilistic methods (Bayesian inference, state-space filtering, change-detection tests, etc.) that flag when fresh weather guidance materially diverges from prior outlooks. Continuous Development and Improvement: Calibrate confidence … of a degree in Statistics, Applied Maths, Physics or related field. Minimum of 2 years working in a Data Science related role Proven depth in probability & inference (e.g., Bayesian updating, time-series/state-space models, extreme-value theory). Hands-on Python for numerical analysis (numpy, pandas, xarray, SciPy/PyMC/PyTorch, or similar). More ❯

is essential. Experience with Regression based models in an MMM context. Strong understanding of statistical modelling/Machine Learning techniques. Experience with probabilistic programming and Bayesian methods Good working knowledge of cloud-based data science frameworks. This is a 6 month contract position which provides a daily rate of £688 (Inside IR35). In terms of More ❯

magnetometry technology that will solve pressing challenges in aeronautical positioning and navigation, geophysical surveying, and magnetic anomaly detection. A key focus of the role will be developing and implementing methods for removing magnetic and/or vibrational noise induced by the platform on which the quantum sensor is mounted. The algorithms, signal processing, automation, and data analytics you develop … analysis and develop platform and environmental noise compensation techniques that enable high quality magnetic- and gravity-aided navigation using state-of-the-art digital signal processing and machine learning methods; Use data analytics to translate quantum sensor data streams into capabilities that solve pressing challenges for current and prospective end-users and customers; Work closely with other Research and … and/or platform motional characterisation and management; magnetic and/or vibrational signal denoising; quantum sensing and/or other sensing technologies. Knowledge and experience of machine learning methods to time series data including generative modelling. Contributing to a software system architecture with multiple components developed by different teams, adhering to best-practice software development precepts. Experience communicating More ❯

sensing, or statistical modelling 3+ years of experience in climate risk, catastrophe, or related modelling domains Bonus Experience Expertise in exposure and vulnerability within catastrophe models Background in Bayesian statistics, Extreme Value Theory, or uncertainty quantification Knowledge of machine learning techniques for climate risk Familiarity with cloud computing (AWS, GCP) ? Hybrid working (3 days/week on More ❯

growing team focused on quantum sensing for a variety of industry sectors such as aerospace, defence, geophysical exploration and Earth observation. Perform essential research and development into performance-enhancing methods for next-generation fielded quantum magnetometers. Develop novel theoretical models and numerical simulation tools suitable for assessing the performance of quantum sensors in real-world environments. Develop platform and … discipline. Experience in the theory, numerical modelling, and/or optimization of one or more of the following disciplines: quantum atomic physics; quantum magnetometry; quantum control; real-time Bayesian estimation (e.g. particle filtering). Finally, you will have a strong desire to work with a world leading team and a company that is fundamentally building the future … quantum technology industry. It would be fantastic if you have these skills but not essential: Familiarity with the Python programming language for scientific computing. Experience in modern signal processing methods (e.g. Bayesian methods, particle filtering, sequential Monte Carlo methods); Experience in sensor modelling and sensor signal processing, including data fusion;Experience modelling optically-pumped More ❯

General Theory for Big Bayes" and Grenoble IDEX. 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 … 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 posterior distribution approximations. State of the art The field of machine learning has recently been much impacted by … processing, to cite just a 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 the same time, the theory More ❯