25 of 25 Permanent Bayesian Methods Jobs in the UK excluding London

testing and test statistics Highly numerate and data-driven Excel skills (can maintain complex spreadsheets) Experience analysing customer trends and behaviours Good understanding of frequentist and/or Bayesian analysis methodologies Additional Information Benefits package includes: Hybrid working Great compensation package and discretionary bonus Core benefits include pension, bupa healthcare, sharesave scheme and more 25 days annual More ❯

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 ❯

for the LGBTQ+ business community. Please do not contact the recruiter directly. Summary Understand complex and critical business problems, formulate integrated analytical approaches to mine data sources, employ statistical methods and machine learning algorithms to help solve unmet medical needs, discover actionable insights, and automate processes to reduce effort and time for repeated use. Manage the implementation and adherence … science, biostatistics, or a related quantitative field (or equivalent). Over 3 years of experience in clinical drug development with extensive exposure to clinical trials. Strong knowledge of statistical methods such as survival analysis, machine learning, meta-analysis, mixed-effects modeling, Bayesian methods, and variable selection techniques. Proficiency in R and Python, with experience in 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 ❯

please apply. Responsibilities Design, build and scale supervised ML models for active learning and Bayesian Optimization of materials synthesis and performance Implement best practices and innovate methods for uncertainty quantification Combine datasets of multiple fidelities and sources to power data-driven materials discovery Work with the computational team to identify materials design pathways that target desired … conferences, and developing relationships with key opinion leaders Report findings to stakeholders and leadership in written reports and verbal presentations. Qualifications Experience with uncertainty quantification, active learning and Bayesian Optimization Experience implementing, evaluating, and hyperparameter tuning small and large supervised models in a Bayesian Optimization context (Gaussian processes, Bayesian Neural Networks 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 ❯

Value, Scanning Metrics from Pose Data Lead the development and implementation of cutting-edge predictive models, machine learning and deep learning algorithms, and statistical techniques (e.g. Markov chain & Bayesian Inference). Enhanced feature development through biomechanical and physics-based modelling Provide leadership in designing and implementing robust R&D infrastructure, ensuring scalability, security, and efficiency in handling 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 ❯

machine learning techniques, such as clustering, classification, and regression Previous experience presenting information to key stakeholders Nice to haves...... Previous experience using Generalised Linear Regression Modelling Knowledge of Bayesian Statistics and Monte Carlo Simulation Experience with Azure: Azure Databases, Azure Databricks Proficient use of VBA's Apply for this job #J-18808-Ljbffr More ❯

machine learning techniques, such as clustering, classification, and regression Previous experience presenting information to key stakeholders Nice to haves...... Previous experience using Generalised Linear Regression Modelling Knowledge of Bayesian Statistics and Monte Carlo Simulation Experience with Azure: Azure Databases, Azure Databricks Proficient use of VBA's Please note due to the large volume of applications we receive More ❯

management Key Experience: Strong academic background with a minimum 2:1 Degree in Computer Science (or similar) Strong numerical background with a knowledge of key statistical principles – eg Bayesian and frequentist statistics Experience with multiple programming languages including SQL and Python Familiarity with large language models Understanding of ML algorithms This is an excellent opportunity for a 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 ❯

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 ❯

Skills and Experience Strong academic background, with a degree in Computer Science (2:1 and above). Strong numerical background with a knowledge of key statistical principals e.g. Bayesian and frequentist statistics, probability distributions. Fundamental understanding of ML algorithms e.g. linear and logistic regression, random forest, neural networks, time series models. Experience with multiple programming languages, with More ❯

Skills and Experience Strong academic background, with a degree in Computer Science (2:1 and above). Strong numerical background with a knowledge of key statistical principals e.g. Bayesian and frequentist statistics, probability distributions. Fundamental understanding of ML algorithms e.g. linear and logistic regression, random forest, neural networks, time series models. Experience with multiple programming languages, with More ❯

Skills and Experience Strong academic background, with a degree in Computer Science (2:1 and above). Strong numerical background with a knowledge of key statistical principals e.g. Bayesian and frequentist statistics, probability distributions. Fundamental understanding of ML algorithms e.g. linear and logistic regression, random forest, neural networks, time series models. Experience with multiple programming languages, with More ❯

Self-motivated, enthusiastic, and organised, with excellent attention to detail Proactive and delivers to timescales Desirable Attention to detail in creating documentation The ability to conduct descriptive statistics, Bayesian statistics, Inferential statistics, Probability distributions and theory, dimensionality reduction and sampling etc Strong numerical and statistical skills Additional Requirements Essential Willing to be flexible in working practices Improvement 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 ❯

Postdoc in Bayesian machine learning, AstraZeneca, Cambridge, UK Mar 29, 2018 PREDICTING DRUG TOXICITY WITH BAYESIAN MACHINE LEARNING MODELS We're currently looking for talented scientists to join our innovative academic-style Postdoc. From our centre in Cambridge, UK you'll be in a global pharmaceutical environment, contributing to live projects right from the … advisor, who'll provide you with the guidance and knowledge you need to develop your career. You will be part of the Quantitative Biology group and develop comprehensive Bayesian machine learning models for predicting drug toxicity in liver, heart, and other organs. This includes predicting the mechanism as well as the probability of toxicity by incorporating scientific … will be used to account for uncertainty in the inputs and propagate this uncertainty into the predictions. In addition, you will promote the use of Bayesian methods across safety pharmacology and biology more generally. You are also expected to present your findings at key conferences and in leading publications This project is in collaboration with Prof. More ❯

fill most positions now but leave some for future years as well: - Research Fellow - Postdoc - PhD Student The work involves probabilistic modelling in exciting new settings, and developing new methods for probabilistic machine learning and inference. Applicants with outstandingly strong expertise in one of following topics are welcome, or strong expertise in one and keen interest in working with … expert colleagues on the others: automatic experimental design, Bayesian inference, human-in-the-loop learning, advanced user modelling, machine teaching, privacy-preserving learning, reinforcement learning, inverse reinforcement learning, simulator-based inference, likelihood-free inference. There will be particularly good opportunities to join new work on collaborative modelling and decision-making with AI. And applications in drug design 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 ❯

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 ❯

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 ❯

Who You Are - Proven track record leveraging machine learning to solve real-world problems; Expertise in one or more of the following: generative models, language models, computer vision, bayesian inference, causal reasoning & inference, transfer & multi-task learning, diffusion models, graph neural networks, active learning; Experience writing production-quality code with modern machine learning frameworks such as PyTorch … Experience in cell health and rejuvenation-related research area; Experience with identification and assessment of drug targets and/or therapeutic compounds; Experience in the application of machine learning methods to biological data, including genomics, transcriptomics, epigenetics, proteomics, and imaging; Track record in open-source software development, e.g., demonstrated by high-impact GitHub repository; Track record of high-caliber More ❯

maintenance. The ideal candidate will be a good communicator and have a growth mindset with an enthusiasm for scientific discovery and strong interest in applying a combination of AI methods and mathematical models toward understanding mechanistic aspects of cellular processes with all experimental labs at Altos Labs. The ideal candidate is particularly interested in multi-scale (systems) biochemistry and … loop between experimental and theoretical work, at multiple scales, from molecules to cells, tissues and even whole organisms. Working at the interface between mathematical and computational models, and AI methods, with the aim of establishing design principles of rejuvenated cells. Collaborating with both experimental and computational scientists across Altos. Influence best practices in areas such as Bayesian … Biology, Computational Biology, Computer Science, or closely related field with strong emphasis in biological modeling. Relevant industry and/or academic experience. Expertise and a track record of using methods from artificial intelligence for biological design. Record of applications of dynamical systems to problems of synthetic biology. Record of applications of data driven modeling methods and AI to More ❯