26 to 50 of 147 Bayesian Methods Jobs

core technologies Collaborate with data scientists and engineers to integrate models into pipelines and tools. Stay current with academic and industry advances in temporal modeling and deep learning. Document methods and support reproducibility, validation, and publication where appropriate. Essential Requirements Strong programming and modeling skills in Python (NumPy, PyTorch or TensorFlow, SciPy, pandas). In-depth knowledge of machine … learning for time-series, including RNNs, LSTMs, GRUs, transformers, attention mechanisms. Solid understanding of probabilistic models (HMMs, Bayesian inference, graphical models). Experience designing or adapting dynamic programming algorithms. A graduate degree (PhD or MSc) in Computer Science, Mathematics, Physics, Bioinformatics, or a related field Ability to work in a research-style setting and translate ideas into More ❯

is essential. Experience with Regression based models in an MMM context. Strong understanding of statistical modelling/Machine Learning techniques. Experience with probabilistic programming and Bayesian methods Good working knowledge of cloud-based data science frameworks. This is a 6 month contract position which provides a daily rate of £688 (Inside IR35). In terms of More ❯

is essential. Experience with Regression based models in an MMM context. Strong understanding of statistical modelling/Machine Learning techniques. Experience with probabilistic programming and Bayesian methods Good working knowledge of cloud-based data science frameworks. This is a 6 month contract position which provides a daily rate of £688 (Inside IR35). In terms of More ❯

is essential. Experience with Regression based models in an MMM context. Strong understanding of statistical modelling/Machine Learning techniques. Experience with probabilistic programming and Bayesian methods Good working knowledge of cloud-based data science frameworks. This is a 6 month contract position which provides a daily rate of £688 (Inside IR35). In terms of More ❯

is essential. Experience with Regression based models in an MMM context. Strong understanding of statistical modelling/Machine Learning techniques. Experience with probabilistic programming and Bayesian methods Good working knowledge of cloud-based data science frameworks. This is a 6 month contract position which provides a daily rate of £688 (Inside IR35). In terms of More ❯

health economics, including the conceptualization and development of systematic literature reviews and network meta-analyses (8+ years, ideally in a consultancy environment) Strong basis in fundamental statistical concepts and methods and familiarity with techniques such as the development of predictive equations, survival analysis (including parametric methods), longitudinal data analysis, meta-analysis, mixed treatment comparison, and other hierarchical analysis … techniques Familiarity with machine learning techniques and Bayesian statistics is a plus Strong communication (spoken and written) and problem-solving skills, and an ability to learn quickly Ability to communicate effectively, in non-technical terms, with project team members and clients Experience in managing projects and leading/coaching a project team Keen interest in medical research More ❯

sophisticated pricing algorithms which would enable companies in the entertainment industry to significantly increase profit margins. You'll use a raft of different techniques from timeseries analysis to bayesian statistics, reinforcement learning & Monte Carlo Simulations. Your Experience : You'll likely come from a strong quantitative degree background in Science or Maths and have worked 2-4 years More ❯

in the AI Lab. Responsibilities Develop new ML models and AI techniques Lead on research projects within a global team Review relevant AI/ML literature to identify emerging methods, technologies, and best practices Explore new data sources and discover techniques for best leveraging data Minimum Qualifications A Master's or PhD in a field related to AI/… Architecture or related disciplines Strong background applying Deep Learning techniques (including implementing custom architectures, optimizing model performance, developing novel loss functions, and deploying production-ready solutions) Familiarity in statistical methods for Machine Learning (e.g. Bayesian methods, HMMs, graphical models, dimension reduction, clustering, classification, regression techniques, etc.) Familiarity with PyTorch, TensorFlow, JAX or similar frameworks Strong More ❯

don't tick all the boxes, you'll certainly learn on the job! Essential MSc or PhD, or equivalent experience in a quantitative field Deep theoretical knowledge of statistical methods and ML algorithms and their practical applications. Strong proficiency in SQL and Python, especially with core ML libraries (e.g. scikit-learn, XGBoost, SciPy, PyTorch) Extensive hands-on experience in … version control (e.g. Git), code reviews, CI/CD pipelines, unit testing, and collaborative workflows such as Agile. Experience with optimisation techniques (e.g. gradient descent, Bayesian methods, Lagrangian methods etc) and their practical application. Experience in UK General Insurance (Motor and/or Home), with a solid understanding of pricing foundations and regulatory governance. Familiarity More ❯

technical audiences. A self-starter who thrives in a fast-paced R&D environment. Experience in catastrophe modelling, especially around exposure and vulnerability. Applied statistical skills such as Bayesian statistics, uncertainty quantification, or Extreme Value Theory. Knowledge of machine learning applications in climate risk. Familiarity with cloud platforms (e.g., AWS, Google Cloud). If this role looks More ❯

Who You Are Proven track record leveraging machine learning to solve real-world problems; Expertise in one or more of the following: generative models, language models, computer vision, bayesian inference, causal reasoning & inference, transfer & multi-task learning, diffusion models, graph neural networks, active learning; Experience writing production-quality code with modern machine learning frameworks such as PyTorch … Experience in cell health and rejuvenation-related research area; Experience with identification and assessment of drug targets and/or therapeutic compounds; Experience in the application of machine learning methods to biological data, including genomics, transcriptomics, epigenetics, proteomics, and imaging; Track record in open-source software development, e.g., demonstrated by high-impact GitHub repository; Track record of high-caliber More ❯

Who You Are - Proven track record leveraging machine learning to solve real-world problems; Expertise in one or more of the following: generative models, language models, computer vision, bayesian inference, causal reasoning & inference, transfer & multi-task learning, diffusion models, graph neural networks, active learning; Experience writing production-quality code with modern machine learning frameworks such as PyTorch … Experience in cell health and rejuvenation-related research area; Experience with identification and assessment of drug targets and/or therapeutic compounds; Experience in the application of machine learning methods to biological data, including genomics, transcriptomics, epigenetics, proteomics, and imaging; Track record in open-source software development, e.g., demonstrated by high-impact GitHub repository; Track record of high-caliber More ❯

Skills and Experience Strong academic background, with a degree in Computer Science (2:1 and above). Strong numerical background with a knowledge of key statistical principals e.g. Bayesian and frequentist statistics, probability distributions. Fundamental understanding of ML algorithms e.g. linear and logistic regression, random forest, neural networks, time series models. Experience with multiple programming languages, with More ❯

Skills and Experience Strong academic background, with a degree in Computer Science (2:1 and above). Strong numerical background with a knowledge of key statistical principals e.g. Bayesian and frequentist statistics, probability distributions. Fundamental understanding of ML algorithms e.g. linear and logistic regression, random forest, neural networks, time series models. Experience with multiple programming languages, with More ❯

science, with a strong foundation in machine learning and statistical modeling. Experience developing LSTM-based models for sequential forecasting or behavioral modeling. Familiarity with probabilistic graphical models (e.g., Bayesian networks, Markov random fields) for structured data relationships. Proficiency in programming languages such as Python or R, and experience with data manipulation and visualization tools. Demonstrated ability to More ❯

Skills and Experience Strong academic background, with a degree in Computer Science (2:1 and above). Strong numerical background with a knowledge of key statistical principals e.g. Bayesian and frequentist statistics, probability distributions. Fundamental understanding of ML algorithms e.g. linear and logistic regression, random forest, neural networks, time series models. Experience with multiple programming languages, with More ❯

mathematical competence and proficiency in statistics A solid grasp of essentially all of the standard data science techniques, for example, supervised/unsupervised machine learning, model cross validation, Bayesian inference, time-series analysis, simple NLP, effective SQL database querying, or using/writing simple APIs for models. We regard the ability to develop new algorithms when an More ❯

Understand complex and critical business problems, formulates integrated analytical approach to mine data sources, employ statistical methods and machine learning algorithms to contribute solving unmet medical needs, discover actionable insights and automate process for reducing effort and time for repeated use. To manage the implementation and adherence to the overall data lifecycle of enterprise data from data acquisition or … biostatistics, or other quantitative field (or equivalent). More than 3 years experience in clinical drug development with extensive exposure to clinical trials. Strong knowledge and understanding of statistical methods such as time to event analysis, machine learning, meta-analysis, mixed effect modeling, longitudinal modeling, Bayesian methods, variable selection methods (e.g., lasso, elastic net More ❯

deep-learning frameworks (e.g., TensorFlow, PyTorch). High mathematical competence and proficiency in statistics. Solid understanding of data science techniques such as supervised/unsupervised learning, cross-validation, Bayesian inference, time-series analysis, NLP, SQL, and API development. Ability to develop new algorithms is a fundamental skill. Leadership mindset focused on team growth, mentorship, and fostering a More ❯

magnetometry technology that will solve pressing challenges in aeronautical positioning and navigation, geophysical surveying, and magnetic anomaly detection. A key focus of the role will be developing and implementing methods for removing magnetic and/or vibrational noise induced by the platform on which the quantum sensor is mounted. The algorithms, signal processing, automation, and data analytics you develop … analysis and develop platform and environmental noise compensation techniques that enable high quality magnetic- and gravity-aided navigation using state-of-the-art digital signal processing and machine learning methods; Use data analytics to translate quantum sensor data streams into capabilities that solve pressing challenges for current and prospective end-users and customers; Work closely with other Research and … and/or platform motional characterisation and management; magnetic and/or vibrational signal denoising; quantum sensing and/or other sensing technologies. Knowledge and experience of machine learning methods to time series data including generative modelling. Contributing to a software system architecture with multiple components developed by different teams, adhering to best-practice software development precepts. Experience communicating More ❯

please apply. Responsibilities Design, build and scale supervised ML models for active learning and Bayesian Optimization of materials synthesis and performance Implement best practices and innovate methods for uncertainty quantification Combine datasets of multiple fidelities and sources to power data-driven materials discovery Work with the computational team to identify materials design pathways that target desired … conferences, and developing relationships with key opinion leaders Report findings to stakeholders and leadership in written reports and verbal presentations. Qualifications Experience with uncertainty quantification, active learning and Bayesian Optimization Experience implementing, evaluating, and hyperparameter tuning small and large supervised models in a Bayesian Optimization context (Gaussian processes, Bayesian Neural Networks More ❯

and computational chemistry for drug discovery. The successful candidate will demonstrate strong abilities in cheminformatics and/or bioinformatics, including knowledge of established techniques and cutting-edge machine learning methods for modeling molecular properties and interactions with complex systems. They will also have experience with scientific programming and data science. These skills will be leveraged within a seasoned, agile … experience including hands-on experience with informatics, machine learning, and computational chemistry applied to drug discovery in the private sector, like biotech or pharma Experience with cheminformatics and bioinformatics methods (e.g., similarity/substructure searching, reaction-based enumeration, sequence alignment, etc.) Experience with molecular property prediction and multi-objective optimization using machine learning and/or deep learning methods … high-performance computing (GPU) environments for corporate R&D, innovation labs, or academic research An interest in solving scientific problems in chemistry and biology via computational and data-driven methods A drive to cooperate with colleagues to identify problems and communicate technical solutions in an accessible manner Hands-on mentality & comfortable with getting deep into the technical weeds of More ❯

team. We are focused on advancing the state-of-the-art design tools to allow architects, artists, designers, and engineers to “Make Anything”. Our research centers around generative methods for design and spans physics-based modeling, simulation, optimization and systems modeling. You will lead and help build build a group focusing on the future of physics informed AI. … record as a practitioner of ML, creating, training and leading groups that can build and train models at scale Excellent written and oral communication skills Excellent knowledge of Numerical Methods Ability to quickly learn new technologies and adapt to new situations Ability to collaborate effectively with a diverse, multicultural global team of scientists, engineers and architects Preferred Qualifications Knowledge … team. We are focused on advancing the state-of-the-art design tools to allow architects, artists, designers, and engineers to “Make Anything”. Our research centers around generative methods for design and spans physics-based modeling, simulation, optimization and systems modeling. You will lead and help build build a group focusing on the future of physics informed AI. More ❯

machine learning techniques, such as clustering, classification, and regression Previous experience presenting information to key stakeholders Nice to haves...... Previous experience using Generalised Linear Regression Modelling Knowledge of Bayesian Statistics and Monte Carlo Simulation Experience with Azure: Azure Databases, Azure Databricks Proficient use of VBA's Apply for this job #J-18808-Ljbffr More ❯

don't tick all the boxes, you'll certainly learn on the job! Essential MSc or PhD, or equivalent experience in a quantitative field Deep theoretical knowledge of statistical methods and ML algorithms and their practical applications. Strong proficiency in SQL and Python, especially with core ML libraries (e.g. scikit-learn, XGBoost, SciPy, PyTorch) Extensive hands-on experience in … version control (e.g. Git), code reviews, CI/CD pipelines, unit testing, and collaborative workflows such as Agile. Experience with optimisation techniques (e.g. gradient descent, Bayesian methods, Lagrangian methods etc) and their practical application Experience in UK General Insurance (Motor and/or Home), with a solid understanding of pricing principles and regulatory governance. Familiarity More ❯