7 of 7 Permanent Probabilistic Modelling Jobs in England

daily directed guidance stands to redefine primary care while helping people live happier, healthier, and longer. Job Summary: We're seeking a Bayesian Data Scientist with deep expertise in probabilistic modeling and a strong grasp of modern AI advancements, including foundation models , generative AI , and variational inference . This role is perfect for someone who thrives on solving complex … driving real business impact. Location: Remote/Hybrid/USA-SF, USA-remote, UK-London, UK-remote Responsibilities: Translate predictive modeling problems and business constraints into robust Bayesian or probabilistic AI solutions. Design and implement reusable libraries of predictive features and probabilistic representations for diverse ML tasks. Build and optimize tools for scalable probabilistic inference under memory … learning analyses, simulations, and experimental design. Stay current with emerging trends in generative modeling, causality, uncertainty quantification, and responsible AI. Requirements/Qualifications: Strong experience in Bayesian inference and probabilistic modeling : PGMs, HMMs, GPs, MCMC, variational methods, EM algorithms, etc. Proficiency in Python (must) and familiarity with PyMC, NumPyro, TensorFlow Probability , or similar probabilistic programming tools. Hands-on More ❯

experience with ML frameworks (e.g. PyTorch, TensorFlow, scikit-learn) Expertise in one or more of the following areas: Multi-modal data fusion or time series analysis Uncertainty quantification or probabilistic modelling Active learning, explainable AI, or online learning Geospatial data analysis or sensor-based modelling Experience preparing technical deliverables and engaging with stakeholders Eligible for SC Clearance More ❯

experience with ML frameworks (e.g. PyTorch, TensorFlow, scikit-learn) Expertise in one or more of the following areas: Multi-modal data fusion or time series analysis Uncertainty quantification or probabilistic modelling Active learning, explainable AI, or online learning Geospatial data analysis or sensor-based modelling Experience preparing technical deliverables and engaging with stakeholders Eligible for SC Clearance More ❯

to run semi-technical interviews, assessing how candidates apply knowledge, explain ideas, and contribute in practice. Bonus: Experience hiring across other technical roles, including data scientists (especially those using probabilistic models), data engineers, product managers, and front-end or full-stack developers. Ability to test new sourcing tools and methods, and adapt based on evidence and feedback. Confidence using More ❯

to run semi-technical interviews, assessing how candidates apply knowledge, explain ideas, and contribute in practice. Bonus: Experience hiring across other technical roles, including data scientists (especially those using probabilistic models), data engineers, product managers, and front-end or full-stack developers. Ability to test new sourcing tools and methods, and adapt based on evidence and feedback. Confidence using More ❯

Positions are available at all stages; we seek to 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 More ❯

a team with access to cutting-edge multiomic and interventional datasets, advanced computational infrastructure, and deep interdisciplinary expertise. This is an opportunity to push the boundaries of what causal modelling can achieve in complex, high-dimensional, and noisy real-world systems, and to see your work tested directly in experimental biology. Your responsibilities Collaborate with domain experts to translate … biological hypotheses into formal causal modelling problems. Design and implement causal learning approaches that capture regulatory logic, cell fate trajectories, and intervention effects from diverse biological data, including single-cell perturbation experiments. Develop models that go beyond correlation, focusing on generalisation, counterfactual prediction, and experimental design. Collaborate with experimental teams to design and validate computational hypotheses via iterative strategies … science or a related quantitative field. Deep expertise in causal inference, such as causal graphical models, counterfactual reasoning, or invariant representation learning. Strong background in one or more of probabilistic modelling, time series analysis, or dynamical systems. Proficiency in Python and familiarity with scalable ML tooling and high-performance computing. Desirable knowledge or experiences Familiarity with biological datasets More ❯