1 to 25 of 29 Permanent Bayesian Methods Jobs in the UK excluding London

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 … 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 ...

deployment in laboratories, hospitals, and public health organisations worldwide. Your Role: As a (Senior) Scientist, you will design, develop, and evaluate novel computational methods for translating metagenomic sequencing data into actionable results in a clinical or public health context. You will develop algorithms and AI/ML methods … Develop ML/AI techniques to address real-world problems in the pathogen genomics space, in collaboration with scientists across EIT Validate and benchmark methods using public and internal datasetsWork with bioinformaticians and software engineers to implement methods in scalable, reproducible, and modular workflows Communicate technical work clearly ...

and machine learning. Statistics & Math: Deep understanding of statistical inference, regression analysis, probability theory, decision trees, and hypothesis testing (p-values, confidence intervals, Bayesian testing etc.). Data Visualization: Hands‐on experience building interactive dashboards using tools like Power BI, Looker, or Python libraries. Experience: 3+ years ...

both technical and non-technical audiences. A proven track record of turning business problems into measurable outcomes. Nice to Have Experience with Bayesian modelling, optimisation algorithms or multi-armed bandits. Exposure to Generative AI, LLMs or agentic AI systems. MLOps experience, including CI/CD, model monitoring ...

stakeholders Hands-on experience with a range of models, such as time-series forecasting, gradient boosting, clustering, NLP and Bayesian methods Strong MLOps: orchestration, experiment tracking, hyperparameter tuning and automated retraining pipelines Object-oriented Python, Polars or PySpark, and data versioning (e.g. DVC), plus experience designing ...

pricing models.* Turning pricing opportunities into measurable hypotheses, experiments and production changes.* Evaluating pricing performance through backtesting, online testing and commercial metrics.* Developing Bayesian, multi-armed bandit and other optimisation approaches for dynamic pricing.* Working with commercial, finance and product teams to understand pricing performance and recommend … code, testing and code review, alongside strong SQL.* A solid grounding in machine learning techniques such as gradient boosting, GLMs, Bayesian methods and multi-armed bandits, or comparable experimentation and optimisation methods.* Experience designing, running and evaluating pricing experiments using both commercial and statistical measures. ...

attribution and customer acquisition economics. Commercially minded, with the ability to influence marketing strategy. An AI-first approach to data science. Experience with Bayesian modelling, value-based bidding or LTV modelling would be advantageous. Package: £70,000-£90,000 + bonus and benefits Location: Fully remote within ...

some ‘nice to haves’ are: Experience with UI/UX design principles. Experience with PyTorch or other deep-learning libraries. An understanding of Bayesian statistics. Location: This role is on-site at our Exeter or Bristol office. As an ambitious, rapidly-growing start-up, we’re looking ...

some ‘nice to haves’ are: Experience with UI/UX design principles. Experience with PyTorch or other deep-learning libraries. An understanding of Bayesian statistics. Location: This role is on-site at our Exeter or Bristol office. As an ambitious, rapidly-growing start-up, we’re looking ...

reasoning with practical software engineering to calibrate models against measurements, test predictions on independent cases and build uncertainty analysis into the simulation workflow. Your methods and software will help engineers and investors understand what the simulator predicts and the limits of those predictions. We welcome people from AI systems … and other fields where simulation is tested against experiments. You might bring systems engineering experience and an interest in developing uncertainty methods, or experience in mathematical modelling and scientific computing that you want to apply to AI infrastructure. What you will do Develop and implement methods to calibrate ...

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 ...

machine learning and AI technologies (NumPy, SciPy, Pandas). Experience with DevOps workflows. Experience with PyTorch or other deep-learning frameworks. Familiarity with Bayesian statistics or probabilistic modelling. Experience with MLOps. Location: This role is on-site at our Exeter or Bristol office. As an ambitious, rapidly ...

sitting at the intersection of modern machine learning and quantum computing — with two complementary directions. ML for quantum — using neural networks and learning-based methods to accelerate quantum computation (e.g. circuit optimisation and compilation, error mitigation, calibration, variational-parameter optimisation, and surrogate modelling of quantum processes). …/with quantum — building and adapting classical AI methods, including neural networks and large language models, so they can benefit from quantum hardware and algorithms. You don't need to be a world expert in either quantum or LLMs — but you should be genuinely comfortable with neural networks and ...

used to understand next-generation energy and thermal management systems. We use techniques like reinforcement learning, multi-objective optimization, and Bayesian methods to model the processes involved in bringing new energy technologies to market, manage uncertainty, and optimize decisions. Responsibilities: Bring your computer science/software … other interns, and stakeholders. You will learn: How to scope and manage your own time and project(s) Computer science, machine learning, and AI methods beyond your current course. Qualifications : Studying Bachelors in Computer Science, AI, Physics or a related discipline Software development skills in at least ...

and a training loop, and who has built systems from scratch because they wanted to understand how they work. Key Responsibilities: Build RL, Bayesian and LLM-agent-driven design-space exploration over gem5 and cycle-accurate models, using fast simulation for broad search and detailed models ...

problems and judge what is worth attempting. Desirable Experience applying machine learning to engineering optimization or design exploration, such as surrogate modelling or Bayesian optimization. Familiarity with engine simulation and CAE tools such as GT‐SUITE or Simcenter. Bachelor's degree in engineering or a related technical ...

Trucco (Faculty of Science, Engineering and Business, Department of Computing) at the University of Dundee. In this role you will develop and apply AI methods to electronic patient record data and linked retinal and other imaging data to develop, validate and apply models to predict health outcomes in people … Python, PySpark Knowledge and experience in analysing molecular data including proteomic and genomic data Experience of working with Bayesian statistical methods Active roles as a researcher in data science projects aimed to biomarker discovery UK Visas and Immigration (UKVI) Requirements Any offer of appointment will ...

research, which involves AI4Science and for health, sequential decision making and experimental design under uncertainty, and collaborative AI. Machine learning keywords include Bayesian inference, distribution shifts, generative modelling, human-in-the-loop learning, privacy-preserving learning, reinforcement learning, inverse reinforcement learning, computational rationality and user modelling, and ...

algorithms in R and Python (SAS, JMP, or C#/C++, optional) Research, develop, and operationalize new statistical, machine learning and/or optimization methods Implement and improve forecasting algorithms Investigate applications of Cheminformatics modelling Investigate active learning for process optimization Scheduling and inventory optimization Accelerate process and product … development cycle using emerging AI methods (such as LLM, RAG, Agentic AI) Previous Intern Projects Include: Predictive modeling using Bayesian and machine learning methods R/Shiny tool development to enable model predictions and formulation optimization Creation of an interactive visualization tool for monitoring predictive ...

Postdoc and Research Fellow positions in Machine Learning, Centre for AI Fundamentals, Manchester, UK Keywords include automatic experimental design, Bayesian inference, human-in-the-loop learning, machine teaching, privacy-preserving learning, reinforcement learning, inverse reinforcement learning, computational rationality and user modelling, and simulator-based inference. More background ...

International Society for Bayesian Analysis is seeking to fill research positions in machine learning at various levels at the University of Manchester. Candidates will engage in groundbreaking research funded by the Turing AI World-Leading Researcher Fellowship, focusing on probabilistic modelling in diverse areas including human ...

Fundamentals, Manchester, UK. These lectureships form part of a strategic investment in fundamental AI research; candidates should have strong backgrounds in Bayesian inference, automatic experimental design, human-in-the-loop learning, machine teaching, privacy-preserving learning, reinforcement learning, and related areas. #J-18808-Ljbffr ...

experimentation (Firebase, Optimizely, GrowthBook, Statsig or similar) and web experimentation on multi-tenant or white-label sites. Sequential or Bayesian test methods, or quasi-experiments where a true A/B test isn’t possible (for example, market-level or restaurant-level rollouts). Paid media ...

field or professional IT certificate. Experience with version control and remote repositories. Experience with data warehouses and open database connectivity. Working knowledge of statistical methods, including both frequentist and Bayesian approaches. Ability to interpret, present and communicate statistical results in line with recognised reporting standards and ...

project leads. Apply modern deep learning for molecular design (GNNs, generative models, property prediction) where it complements traditional CADD methods. Contribute to productionising CADD methods into a reproducible, API-first toolkit; partner with Infrastructure on cost-effective GPU/HPC workflows. Own the computational design strategy on assigned programmes … Working knowledge of modern deep learning for molecular design (GNNs, generative models, property prediction), and a clear sense of when these complement traditional CADD methods rather than replace them. (Senior level) A track record of owning the computational strategy on active programmes, and of technical leadership — mentoring and influencing ...