51 to 75 of 78 Bayesian Methods Jobs

in their own abilities, be happy to work alone and as part of a small team and have excellent communication and problem-solving skills. Familiarity and confidence in Bayesian statistics and simulation techniques, including experience of programming in Python or R, is essential. The department has gained a Silver Athena SWAN award as a commitment to providing More ❯

Data Science. These are both open-ended positions. Applicants are invited from any area of applied statistics, including statistical or actuarial data science. Those working in actuarial science, Bayesian statistics, statistical learning and/or actuarial statistics are particularly welcome to apply. Candidates who would like to work with the University's multi-disciplinary Global Research Institutes … internationally excellent research in your field. You will have a strong track record of research in applied statistics, actuarial or statistical data science which includes fields such as Bayesian statistics, statistical learning, actuarial statistics, computational statistics and statistical methodology. You will either be established or have the potential to establish yourself as an international research leader, with More ❯

A high level of mathematical competence. The ability to code or have programming experience, especially in Python. Some experience with theoretical concepts of statistical learning (e.g. hypothesis testing, Bayesian Inference, Regression, SVM, Random Forests, Neural Networks, Natural Language Processing, optimisation). Experience with some coding libraries frequently used in data science. The ability to communicate effectively. Experience More ❯

evolve an internal experimentation platform used company-wide Own and scale the stats engines and inference tools that power experiment analysis Build and apply advanced statistical models – especially Bayesian and casual inference approaches Help design new features that make large-scale experimentation easy Act as a go-to mentor for casual inference and experimentation, mentoring others Collaborate … cross-functional teams to keep improving experiments They’re looking for: 5+ years' experience in data science, with at least 3 years at senior level Strong expertise in Bayesian statistics, causal inference, and experimental design Advanced Python programming skills and fluency in SQL Hands-on experience building or contributing to experimentation platforms or data infrastructure Familiarity with More ❯

evolve an internal experimentation platform used company-wide Own and scale the stats engines and inference tools that power experiment analysis Build and apply advanced statistical models – especially Bayesian and casual inference approaches Help design new features that make large-scale experimentation easy Act as a go-to mentor for casual inference and experimentation, mentoring others Collaborate … cross-functional teams to keep improving experiments They’re looking for: 5+ years' experience in data science, with at least 3 years at senior level Strong expertise in Bayesian statistics, causal inference, and experimental design Advanced Python programming skills and fluency in SQL Hands-on experience building or contributing to experimentation platforms or data infrastructure Familiarity with More ❯

Theo Damoulas and Prof. Mark Steel, as part of the Turing-Lloyds Register Foundation funded project 'Air Quality Sensor Networks'. This project is likely to involve hierarchical Bayesian models, nonparametric Bayesian inference, graphical models, active learning, experimental design and issues in spatio-temporal inference such as non-stationarity and non-separability. The expectation … or Computer Science or Applied Mathematics (or you will shortly be obtaining it). You should have a strong background in one or more of the following areas: Bayesian inference, spatial statistics, probabilistic machine learning. The post is based in the Departments of Statistics and Computer Science (joint appointment) at the University of Warwick, but the work More ❯

and implementing advanced mathematical models that drive the ToffeeX engineering design software. You will collaborate with a multidisciplinary team to solve complex engineering problems, focusing on mathematical optimization, numerical methods for partial differential equations (PDEs), and computational geometry. This role offers the opportunity to work at the intersection of advanced mathematics, engineering, and technology in a rapidly growing company. … literature, developing creative approaches to solve unique real-world engineering problems. Essential Skills & Experience Ph.D. in Applied Mathematics, Physics, Engineering, or a closely related field. Research level experiencein numerical methods for PDEs, mathematical modelling or PDE-constrained optimization. Hands-on experience in developing and implementing numerical algorithms for solving complex simulation or optimization problems. You thrive in a fast … for physical systems. Experience in numerical optimization topics such as nonconvex, robust, or Bayesian optimization. Experience with topology or shape optimization. Experience with AI/ML methods for physics or optimization problems. Experience with software development in C++, Python, or similar languages. Company Benefits Flexibility and support for continuous learning and professional growth. 25 days holiday More ❯

development of multiple proprietary betting models. Job Requirements: 2+ years' experience (ideally in a syndicate) developing sports betting models from scratch, in any sport Strong fundamental understanding of Bayesian statistics, variational inference, filtering techniques, state space and hierarchical models MSc+ level degree from well respected university in any STEM subject Excellent proficiency in any programming language, but More ❯

development of multiple proprietary betting models. Job Requirements: 2+ years' experience (ideally in a syndicate) developing sports betting models from scratch, in any sport Strong fundamental understanding of Bayesian statistics, variational inference, filtering techniques, state space and hierarchical models MSc+ level degree from well respected university in any STEM subject Excellent proficiency in any programming language, but More ❯

Physics, or similar). Experience building and maintaining predictive credit models. Fluency in Python (Pandas, NumPy, SciPy, Matplotlib) and SQL. Experience with advanced modelling techniques (e.g., Monte Carlo, Bayesian modelling) is a plus. Strong communicator. Commercial mindset and strong instincts around risk and return. Experience mentoring or managing analysts. Knowledge of the German lending market or language More ❯

Physics, or similar). Experience building and maintaining predictive credit models. Fluency in Python (Pandas, NumPy, SciPy, Matplotlib) and SQL. Experience with advanced modelling techniques (e.g., Monte Carlo, Bayesian modelling) is a plus. Strong communicator. Commercial mindset and strong instincts around risk and return. Experience mentoring or managing analysts. Knowledge of the German lending market or language More ❯

Physics, or similar). Experience building and maintaining predictive credit models. Fluency in Python (Pandas, NumPy, SciPy, Matplotlib) and SQL. Experience with advanced modelling techniques (e.g., Monte Carlo, Bayesian modelling) is a plus. Strong communicator. Commercial mindset and strong instincts around risk and return. Experience mentoring or managing analysts. Knowledge of the German lending market or language More ❯

architecture to capture subtle market signals. They will rely on your deep knowledge of deep learning, whether your background is in LLMs, recsys, image models, RL agents, or classical methods, to help shape the next generation of their ML-driven trading. You’ll also contribute to hiring, mentor teammates, and share insights from the broader research community through papers … experience in industry applying ML to challenging problems Expertise in one or more of: deep learning, reinforcement learning, non-convex optimisation, approximate inference, NLP, or Bayesian methods Strong programming skills, ideally in Python, with experience using tools like NumPy, Pandas, JAX, PyTorch or TensorFlow A strong publication record in top-tier venues (e.g., NeurIPS, ICML, ICLR More ❯

architecture to capture subtle market signals. They will rely on your deep knowledge of deep learning, whether your background is in LLMs, recsys, image models, RL agents, or classical methods, to help shape the next generation of their ML-driven trading. You’ll also contribute to hiring, mentor teammates, and share insights from the broader research community through papers … experience in industry applying ML to challenging problems Expertise in one or more of: deep learning, reinforcement learning, non-convex optimisation, approximate inference, NLP, or Bayesian methods Strong programming skills, ideally in Python, with experience using tools like NumPy, Pandas, JAX, PyTorch or TensorFlow A strong publication record in top-tier venues (e.g., NeurIPS, ICML, ICLR More ❯

and engineers to build AI systems tailored for biomedical discovery Contribute to long-term research programs focused on autonomous AI agents Lead and implement state-of-the-art ML methods into a therapeutic platform Publish high-impact research and support the development of patents Your Experience MSc (or equivalent) in Machine Learning, Computer Science, or related field Proven track … record with LLMs, generative AI, optimization, or sequential learning Experience with deep learning frameworks (TensorFlow, PyTorch, JAX, etc.) Expertise in one or more: reinforcement learning, Bayesian optimization, optimal experimental design, agents, or causality Experience in drug/target discovery, treatment modeling, or medical AI research Publications in high-impact conferences or journals highly desirable Next Steps This More ❯

and engineers to build AI systems tailored for biomedical discovery Contribute to long-term research programs focused on autonomous AI agents Lead and implement state-of-the-art ML methods into a therapeutic platform Publish high-impact research and support the development of patents Your Experience MSc (or equivalent) in Machine Learning, Computer Science, or related field Proven track … record with LLMs, generative AI, optimization, or sequential learning Experience with deep learning frameworks (TensorFlow, PyTorch, JAX, etc.) Expertise in one or more: reinforcement learning, Bayesian optimization, optimal experimental design, agents, or causality Experience in drug/target discovery, treatment modeling, or medical AI research Publications in high-impact conferences or journals highly desirable Next Steps This More ❯

firm with startup-style freedom to build sports models. Role Overview Develop American football, baseball, tennis & ice hockey models & more Price same-game parlays using advanced stats Apply Bayesian inference, filtering & mathematical modeling Work with quant engineers on dashboards & implementation Ideal Candidate Strong quant background (Bachelor’s/Master’s; PhD a plus) Experience in syndicate or More ❯

firm with startup-style freedom to build sports models. Role Overview Develop American football, baseball, tennis & ice hockey models & more Price same-game parlays using advanced stats Apply Bayesian inference, filtering & mathematical modeling Work with quant engineers on dashboards & implementation Ideal Candidate Strong quant background (Bachelor’s/Master’s; PhD a plus) Experience in syndicate or More ❯

growing team focused on quantum sensing for a variety of industry sectors such as aerospace, defence, geophysical exploration and Earth observation. Perform essential research and development into performance-enhancing methods for next-generation fielded quantum magnetometers. Develop novel theoretical models and numerical simulation tools suitable for assessing the performance of quantum sensors in real-world environments. Develop platform and … discipline. Experience in the theory, numerical modelling, and/or optimization of one or more of the following disciplines: quantum atomic physics; quantum magnetometry; quantum control; real-time Bayesian estimation (e.g. particle filtering). Finally, you will have a strong desire to work with a world leading team and a company that is fundamentally building the future … quantum technology industry. It would be fantastic if you have these skills but not essential: Familiarity with the Python programming language for scientific computing. Experience in modern signal processing methods (e.g. Bayesian methods, particle filtering, sequential Monte Carlo methods); Experience in sensor modelling and sensor signal processing, including data fusion;Experience modelling optically-pumped More ❯

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 and decision-making with AI. And applications in drug design More ❯

importantly, impact patients' lives. Opportunity We're now hiring a Director of Statistical Genetics & Functional Genomics to lead a growing team that integrates human genetics, functional genomics, and computational methods to drive novel target discovery. This is a rare opportunity to help shape our therapeutic pipeline by unlocking the causal mechanisms of disease from human data. Your responsibilities Lead … architecture. Collaborate cross-functionally with disease biology, target discovery, and experimental teams to translate genetic insights into testable hypotheses and novel therapeutic concepts. Stay at the forefront of emerging methods in statistical genetics and variant-to-function inference, guiding internal adoption and development. Contribute to platform strategy and target identification efforts across multiple disease areas. Foster a culture of … and supporting their continued development. Who you are You bring deep expertise in human statistical genetics, including fine-mapping, LD structure, polygenic risk scoring, partitioned heritability, and causal inference methods (e.g. Mendelian Randomisation). You have experience integrating multi-modal data-including GWAS, transcriptomic (bulk and single-cell), epigenomic (ATAC-seq, ChIP-seq), and proteomic datasets-to identify causal More ❯

testing and test statistics Highly numerate and data-driven Excel skills (can maintain complex spreadsheets) Experience analysing customer trends and behaviours Good understanding of frequentist and/or Bayesian analysis methodologies Additional Information Benefits package includes: Hybrid working Great compensation package and discretionary bonus Core benefits include pension, bupa healthcare, sharesave scheme and more 25 days annual More ❯

testing and test statistics Highly numerate and data-driven Excel skills (can maintain complex spreadsheets) Experience analysing customer trends and behaviours Good understanding of frequentist and/or Bayesian analysis methodologies Additional Information Benefits package includes: Hybrid working Great compensation package and discretionary bonus Core benefits include pension, bupa healthcare, sharesave scheme and more 25 days annual More ❯

General Theory for Big Bayes" and Grenoble IDEX. Interested applicants should write to us with: a letter of interest, CV, and should require two recommendation letters. Context Bayesian deep learning brings together two of the most important machine learning paradigms: Bayesian inference and deep learning. On the one hand, Bayesian learning … theoretically sound framework to formalise the estimation of the architecture and the parameters of deep neural network models. On the other hand, deep learning offers new tools in Bayesian modelling, e.g. to learn flexible nonparametric priors or computationally efficient posterior distribution approximations. State of the art The field of machine learning has recently been much impacted by … processing, to cite just a few. While very effective, these models are computationally costly and require large quantities of data for their many parameters to be accurately estimated. Bayesian statistics offers a theoretically well-grounded framework to reason about uncertainty, and it is one of the cornerstones of modern machine learning. At the same time, the theory More ❯

years of experience in predictive modelling, machine learning, and probability theory, preferably in the sports or gaming/betting industries. Familiarity with techniques such as Monte Carlo simulation, Bayesian modelling, mixed effects models, Kalman filters, GLMs, and time series forecasting. Strong programming skills, particularly in Python. Experience in exploring new datasets, identifying data quality issues, and handling More ❯