21 of 21 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 ...

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 ...

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 ...

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. ...

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 ...

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 ...

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 ...

world physical systems. Must-Have Requirements Strong Python experience, particularly for data pipelines. Experience with probabilistic time-series forecasting , including Bayesian methods, PyMC, Amazon DeepAR or similar. Experience working with small or scarce datasets for distribution prediction. Proven ability to build data pipelines/database architectures ...

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 ...

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 ...

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 ...

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 ...

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 ...

Postgraduate Research Student (funded). DL June 30, 2022 I am hiring researchers to my team working on probabilistic machine learning. Keywords include: Bayesian inference, reinforcement learning and inverse reinforcement learning, automatic experimental design, multi-agent learning, Bayesian deep learning, amortized inference, human ...

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 ...

also offers clear scope for growth: the role-holder will be encouraged to engage directly in the group’s research, helping develop new computational methods and contributing to publications, with responsibilities that can expand as the Cambridge node grows. Establishing and managing reliable version-control infrastructure for the group … within the group’s codes, together with the research team, profiling and optimising their performance. Investigating, prototyping and implementing machine-learning and advanced statistical methods (e.g. improved sampling for Bayesian parameter inference, surrogate models/emulators, simulation-based inference) to accelerate the group’s modelling and ...

each stage of the consumer’s lifecycle. Experience overseeing experimentation programmes and practices that inform product or business decisions. A good understanding of methods for statistical inference (e.g. Bayesian, Frequentist) and familiarity with predictive analytics techniques is highly beneficial. Working knowledge of common analytics tools used ...