4 of 4 Probabilistic Modelling Jobs in the UK excluding London

Hands-On Development • Define and implement end-to-end AI pipelines: data collection/cleaning, feature engineering, model training, validation, and inference. • Rapidly prototype novel models (e.g., neural networks, probabilistic models) using PyTorch, TensorFlow, JAX, or equivalent. • Productionize models in cloud/on-prem environments (AWS/GCP/Azure) with containerization (Docker/Kubernetes) and ensure low-latency More ❯

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

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 ❯