26 to 35 of 35 Bayesian Methods Jobs in England

localisation and emission estimation. Using your strong analytical and problem-solving skills, you will work with complex real-world datasets from field deployments and apply a combination of Bayesian inference, machine learning , and data-driven modelling techniques to deliver robust, interpretable results. We are looking for a hands-on, experienced data scientist who can quickly understand our … Detection of emission events (signal processing, anomaly detection, ML classification) Gas modelling for source localisation and quantification Total site quantification of emissions Research and apply machine learning and AI methods to complement physics-based models, improve detection sensitivity, and automate performance assessment Take a key role in advancing Mirico’s gas-modelling capability , driving innovation in localisation accuracy and … or sensor data Experience in atmospheric dispersion, Gaussian plume , or advection–diffusion modelling Knowledge of probabilistic ML or hybrid modelling (combining physics and data-driven approaches) Familiarity with Bayesian inference, MCMC , or probabilistic programming frameworks (e.g., PyMC, Stan, or TensorFlow Probability) Operating data science/ML workloads at scale using tools such as Argo Workflows, Prefect or More ❯

localisation and emission estimation. Using your strong analytical and problem-solving skills, you will work with complex real-world datasets from field deployments and apply a combination of Bayesian inference, machine learning , and data-driven modelling techniques to deliver robust, interpretable results. We are looking for a hands-on, experienced data scientist who can quickly understand our … Detection of emission events (signal processing, anomaly detection, ML classification) Gas modelling for source localisation and quantification Total site quantification of emissions Research and apply machine learning and AI methods to complement physics-based models, improve detection sensitivity, and automate performance assessment Take a key role in advancing Mirico’s gas-modelling capability , driving innovation in localisation accuracy and … or sensor data Experience in atmospheric dispersion, Gaussian plume , or advection–diffusion modelling Knowledge of probabilistic ML or hybrid modelling (combining physics and data-driven approaches) Familiarity with Bayesian inference, MCMC , or probabilistic programming frameworks (e.g., PyMC, Stan, or TensorFlow Probability) Operating data science/ML workloads at scale using tools such as Argo Workflows, Prefect or More ❯

Strong coding skills in Python, with deep familiarity across ML and data libraries (pandas, numpy, scipy, scikit-learn, tensorflow, pytorch). A broad understanding of machine learning and statistical methods, including NLP, deep learning, Bayesian inference, time-series forecasting, and recommendation systems. Hands-on experience with cloud platforms such as AWS, GCP, or Azure, and exposure More ❯

Strong coding skills in Python, with deep familiarity across ML and data libraries (pandas, numpy, scipy, scikit-learn, tensorflow, pytorch). A broad understanding of machine learning and statistical methods, including NLP, deep learning, Bayesian inference, time-series forecasting, and recommendation systems. Hands-on experience with cloud platforms such as AWS, GCP, or Azure, and exposure More ❯

maintenance. The ideal candidate will be a good communicator and have a growth mindset with an enthusiasm for scientific discovery and strong interest in applying a combination of AI methods and mathematical models toward understanding mechanistic aspects of cellular processes with all experimental labs at Altos Labs. The ideal candidate is particularly interested in multi-scale (systems) biochemistry and … loop between experimental and theoretical work, at multiple scales, from molecules to cells, tissues and even whole organisms. Working at the interface between mathematical and computational models, and AI methods, with the aim of establishing design principles of rejuvenated cells. Collaborating with both experimental and computational scientists across Altos. Influence best practices in areas such as Bayesian … Biology, Computational Biology, Computer Science, or closely related field with strong emphasis in biological modeling. Relevant industry and/or academic experience. Expertise and a track record of using methods from artificial intelligence for biological design. Record of applications of dynamical systems to problems of synthetic biology. Record of applications of data driven modeling methods and AI to More ❯

Qualifications Master's or PhD in Computer Science, Machine Learning, Data Science, or related field Good foundational understanding and subsequent practical application of ML techniques, preferably NLPs, LLMs, Bayesian Optimisation and MCMCs Strong proficiency in Python and ML frameworks and tools (e.g. PyTorch, TensorFlow, JAX, Hugging Face, vLLM, MCP, prompt engineering) Excellent communication skills and ability to … control tools (i.e. git, GitHub etc.) Experience with data scrapping (e.g. Beautiful Soup), extraction (e.g. LLMs) and cleaning. Knowledge with uncertainty quantification and probabilistic modelling approaches such as Bayesian optimisation Experience with cloud deployment pipelines (Docker, cloud platforms, AWS, CI/CD) Familiarity with sustainability or environmental data standards and frameworks What We Offer Opportunity to make More ❯

Qualifications Master's or PhD in Computer Science, Machine Learning, Data Science, or related field Good foundational understanding and subsequent practical application of ML techniques, preferably NLPs, LLMs, Bayesian Optimisation and MCMCs Strong proficiency in Python and ML frameworks and tools (e.g. PyTorch, TensorFlow, JAX, Hugging Face, vLLM, MCP, prompt engineering) Excellent communication skills and ability to … control tools (i.e. git, GitHub etc.) Experience with data scrapping (e.g. Beautiful Soup), extraction (e.g. LLMs) and cleaning. Knowledge with uncertainty quantification and probabilistic modelling approaches such as Bayesian optimisation Experience with cloud deployment pipelines (Docker, cloud platforms, AWS, CI/CD) Familiarity with sustainability or environmental data standards and frameworks What We Offer Opportunity to make More ❯

our services. Aid in business growth by engaging in client meetings, presentations, and proposals, highlighting the advantages and functionality of our technological offerings. Required Skills: Skilled in various modelling methods such as active learning, transfer learning, agent-based modelling, optimization, Bayesian inference, entity extraction/resolution, and spatio-temporal modelling. Proficient in developing models from fundamental More ❯

Materials - Oxford/Remote (UK) (Tech stack: Python, PyTorch, TensorFlow, Scikit-learn, MLflow, Airflow, Docker, Kubernetes, AWS, Azure, GCP, Pandas, NumPy, SciPy, CI/CD, MLOps, Data Visualization, Bayesian Modelling, Probabilistic Programming, Terraform) We're looking for a Machine Learning Engineer to join a rapidly scaling deep-tech company that's reinventing how the world designs and … all of the following (full training provided to fill any gaps): Python, PyTorch, TensorFlow, Scikit-learn, MLflow, Airflow, Docker, Kubernetes, Pandas, NumPy, SciPy, CI/CD, Data Visualization, Bayesian Modelling, Probabilistic Programming, Terraform, Azure, AWS, GCP, Git, and Agile methodologies. Join a team that's fusing AI, science, and engineering to push the boundaries of what's More ❯

Research Fellows and Postdocs in Probabilistic Machine Learning and Bayesian Inference Aug 11, 2021 Research Fellows and Postdocs in Probabilistic Machine Learning and Bayesian Inference I am looking for researchers to my new team in Manchester, UK, funded by the UKRI Turing AI World-Leading Researcher Fellowship programme "Human-AI Research Teams - Steering AI … in Experimental Design and Decision-Making". Closing date of call: September 6, 2021. The research is fundamental research in probabilistic modelling and Bayesian inference, applied to the exciting problems of how do we steer machine learning systems. Particularly challenging is to steer when we cannot (yet) precisely specify our goal, and ultimately we would like to … design and decisions. I am not expecting anyone to master all of these, though if you do, please apply immediately The team will have excellent opportunities of applying the methods to medicine, especially cancer research and remote medicine; experimental design in synthetic biology and drug design; and digital twins. We have top-notch collaborators in each, both in Academia More ❯