17 of 17 Bayesian Methods Jobs in England

least 3 data or ML products from problem definition to production deployment and monitoring.* Demonstrated proficiency in clustering, predictive modeling, reinforcement learning, and Bayesian statistics.* Hands-on experience in software engineering, MLOps, and deploying machine learning models at scale.* Proficiency in SQL, Python, and familiarity with ...

least 3 data or ML products from problem definition to production deployment and monitoring. Demonstrated proficiency in clustering, predictive modeling, reinforcement learning, and Bayesian statistics. Hands-on experience in software engineering, MLOps, and deploying machine learning models at scale. Proficiency in SQL, Python, and familiarity with ...

coding skills, analytical abilities, and innovative thinking to quickly understand new domains and transform creative ideas into functional solutions. Employ statistical and data science methods to make data‐driven decisions. Manipulate large data sets for business insights and drive actionable solutions. Utilize appropriate methods and approaches to develop … Proven ability to conceptualize business problems and solve them through data science solutions. Proven knowledge of AI techniques such as Bayesian methods, Clustering, Ensemble tree models, NLP, etc., with an excellent grasp of statistical concepts and methods. Good understanding of LLMs, guardrails, RAG, agentic AI. Strong ...

React; Javascript/Typescript. Experience with UI/UX design principles. Experience with PyTorch or other deep‐learning libraries. An understanding of Bayesian statistics. Location On site. As an ambitious, rapidly‐growing start‐up, we’re looking for proactive, adaptable people who thrive in a fast‐paced ...

findings Basic Qualifications PhD in business economics, engineering, analytics, mathematics, statistics, information technology or equivalent Experience in causal modeling like graphical models, causal Bayesian network, potential outcomes, A/B testing, experiments, quasi-experiments, and data science workflows Experience distilling informal customer requirements into problem definitions, dealing ...

natural‐world systems: messy, noisy, sparse, nonlinear, and only partially observed. Nice to Have Experience with mechanistic models, hybrid ML, Bayesian methods, causal inference, dynamical systems, or uncertainty quantification. Experience designing benchmarks in novel or poorly defined problem spaces. Experience building models that move from research ...

with time‐series modelling and industrial or IoT data. You have experience in any of: dynamical systems, reinforcement learning, system identification, optimisation or Bayesian statistics. You are used to working in a fast‐paced startup environment with an agile process. You have a degree in machine learning ...

with time‐series modelling and industrial or IoT data. You have experience in any of: dynamical systems, reinforcement learning, system identification, optimisation or Bayesian statistics. You are used to working in a fast‐paced startup environment with an agile process. You have a degree in machine learning ...

engineers • Some front-end/product sense (you care about shipping usable tools, not just notebooks) • Experience with causal inference, Bayesian methods, or mechanistic simulation ...

products PhD or other experience in a research environment Deep experience in an applicable ML area. E.g. NLP, Deep learning, Bayesian methods, Reinforcement learning, clustering Strong stats or math background Visualization, data skills, SQL, matplotlib, etc. Benefits We are a well treated bunch, with awesome benefits ...

products PhD or other experience in a research environment Deep experience in an applicable ML area. E.g. NLP, Deep learning, Bayesian methods, Reinforcement learning, clustering Strong stats or math background Benefits Competitive salary and equity in a fast‐growing start‐up We serve lunch every weekday ...

edge algorithms that combine scientific and applied innovation. Candidates should have a strong academic background in Computer Science, expertise in reinforcement learning or Bayesian optimisation, and proficiency in Python. This is an exciting opportunity to contribute to high-impact AI projects within a dynamic multinational team. #J ...

unclear. You should apply if... You have deep expertise across a range of ML techniques (e.g. Transformer‐based NLP, Deep Learning, Tree‐based methods, Bayesian modelling) and the judgment to select solutions that balance theoretical soundness against engineering practicality. You have a proven track record ...

algorithms that power Biostream’s physiological monitoring and alert systems. You will work closely with the biostatistics team to translate probabilistic and Bayesian models into deployable algorithms that operate on real‐world sensor data. The role focuses on building robust machine learning systems capable of detecting physiological … constrained connectivity. Responsibilities Design and develop machine learning algorithms for physiological signal analysis and risk detection. Work closely with the biostatistician to implement Bayesian models and probabilistic frameworks. Develop models combining vital signs, motion data, and contextual signals. Build pipelines for sensor data processing, feature extraction, and ...

tools and datasets to characterise small-molecule interactions with intrinsically disordered proteins. Based just a short walk from Kings Cross station, Bind leverages interdisciplinary methods that span experimental biophysics – with a strong focus on nuclear magnetic resonance (NMR) spectroscopy – as well as computational approaches and cellular studies. You will … built models by implementing automated, streamlined, and efficient software solutions in line with best practices Utilise active learning, Bayesian, and bootstrapping methods to achieve robust performance in low-data regimes, and make use of distributed training methodologies for large models Build model-deployment and job-launching ...

bring to the table Ability to scope and effectively deliver projects. Enthusiasm about using machine learning, especially deep learning and/or probabilistic methods, for science and engineering. Strong problem‐solving skills and the ability to analyse issues, identify causes, and recommend solutions quickly. Excellent collaboration and communication skills … applied statistics, mathematics, physics, engineering, or a related field, with particular expertise in any of the following: operator learning (neural operators), or other probabilistic methods for PDEs; geometric deep learning or other 3D computer vision methods for point‐cloud or mesh‐structured data; generative models for geometry and ...

recognition, including Huawei’s Gold Medals and Best Technology Breakthroughs. This role focuses on advancing the state-of-the-art in reinforcement learning, Bayesian optimisation, AI agents, large language models (LLMs), and/or vision-language models (VLMs). You will work at the intersection of fundamental … eagerness to translate cutting-edge ideas into working prototypes and real-world applications. Key Responsibilities Conduct original research in reinforcement learning or Bayesian optimisation for LLMs, leading to publications in top conferences and journals (e.g., NeurIPS, ICLR, ICML). Design and implement new algorithms and models that ...