76 to 100 of 128 Permanent Bayesian Methods Jobs

bookmaker) Experience with and/or knowledge of Bayesian models, state space models, filtering and smoothing, computational statistics and approximate inference methods Experience with and/or knowledge of machine and statistical learning, deep neural networks, feature engineering, reinforcement learning, dynamic optimisation and optimal control Experience ...

governance for innovation deliverables.• Lead and develop statisticians through prioritization, coaching, recruitment input, and succession planning.• Work with Biostatistics and Statistical Programming to ensure methods are feasible, robust, reproducible, and submission-ready.• Represent Biometry in governance, asset reviews, development planning, due diligence, and external scientific discussions.• Build external networks … statistical computing environments, with the ability to guide reproducible modelling and simulation workflows.• Proven ability to influence global cross-functional teams and communicate complex methods, assumptions, uncertainty, limitations, and decisions clearly.Knowledge & Experience (preferred):• Experience with advanced data science methods, including digital twins, virtual/external controls, Bayesian ...

governance for innovation deliverables. Lead and develop statisticians through prioritization, coaching, recruitment input, and succession planning. Work with Biostatistics and Statistical Programming to ensure methods are feasible, robust, reproducible, and submission-ready. Represent Biometry in governance, asset reviews, development planning, due diligence, and external scientific discussions. Build external networks … statistical computing environments, with the ability to guide reproducible modelling and simulation workflows. Proven ability to influence global cross-functional teams and communicate complex methods, assumptions, uncertainty, limitations, and decisions clearly. Knowledge & Experience (preferred): Experience with advanced data science methods, including digital twins, virtual/external controls, Bayesian ...

oversee a small team of quantitative experts delivering advanced trial design, modelling, and predictive analytics. The role drives digital twins, external controls, and Bayesian borrowing, while ensuring regulatory alignment and high-impact use cases. Collaboration with Biometry Leadership is essential. #J-18808-Ljbffr ...

Postdoc and Research Fellow positions in Machine Learning, Centre for AI Fundamentals, Manchester, UK Keywords include automatic experimental design, Bayesian inference, human-in-the-loop learning, machine teaching, privacy-preserving learning, reinforcement learning, inverse reinforcement learning, computational rationality and user modelling, and simulator-based inference. More background ...

engineers, and industry experts to deliver real customer value. The role blends research and production, requiring a strong ML foundation, Bayesian methods, and ability to translate research into practical solutions. Hybrid work in London offered. #J-18808-Ljbffr ...

analyses, develop protocols, and provide senior statistical leadership across multiple engagements with clients. The role requires hands-on expertise in Bayesian methods, ITCs, survival analyses, and programming in R and BUGS/JAGS/Stan. Collaboration across geographies is essential, with mentoring duties and business development ...

International Society for Bayesian Analysis is seeking to fill research positions in machine learning at various levels at the University of Manchester. Candidates will engage in groundbreaking research funded by the Turing AI World-Leading Researcher Fellowship, focusing on probabilistic modelling in diverse areas including human ...

Fundamentals, Manchester, UK. These lectureships form part of a strategic investment in fundamental AI research; candidates should have strong backgrounds in Bayesian inference, automatic experimental design, human-in-the-loop learning, machine teaching, privacy-preserving learning, reinforcement learning, and related areas. #J-18808-Ljbffr ...

built that other people went on to use Broad training in empirical method, potentially from a field outside machine learning – experimental design, Bayesian inference, model selection, significance testing, uncertainty quantification Experience with automated grading and model-based judging, and a rigorous account of its failure modes and ...

with agentic AI frameworks and tooling, such as Bedrock AgentCore, AWS Strands, Azure, and MCP/A2A protocols. Breadth across AI and data science methods, including classical ML, causal inference, optimization, and Bayesian approaches, with comfort working across techniques as problems demand. Proficiency in SQL and ...

Role We are looking for a biostatistician with expertise in Bayesian modelling to work on probabilistic modelling of physiological data and medical risk. The role focuses on developing statistical frameworks that combine multiple data sources to estimate the probability of life-threatening deterioration and support triage prioritisation. … engineers, medical advisors, and the product team to design models that operate in real-world environments with noisy and incomplete data. Responsibilities Develop Bayesian statistical models to estimate physiological deterioration risk from multi-sensor data Build probabilistic frameworks integrating vital signs, haemodynamic indicators, motion data, and environmental ...

organisation. Statistical & Experimentation Expertise: Authoritative command of experimentation in all its forms, from experimental design and variance reduction to causal inference, bandits and Bayesian methods. You should be able to develop and validate methodology, not only apply it. Technical & Modelling Expertise: Expert level proficiency in Python and … and growth over time, and to bring that perspective into how experiments are designed and interpreted. Standardising at Scale: Experience standardising processes, frameworks or methods across multiple teams, including the use of AI and automation to make good practice the default. Cross‐Functional Partnership: Proven ability to build strong ...

statistics, mathematics, computer science, or equivalent experience with experience in researchProven experience applying machine learning techniques to solve business problemsProven experience in translating technical methods to non-technical stakeholdersStrong programming experience in Python and the relevant analytics libraries (e.g., pandas, numpy, matplotlib, scikit-learn, statsmodels, pymc, pytorch/…/keras, langchain). R and C++ optional. Experience with version control (GitHub)ML experience with causality, Bayesian statistics & optimization, survival analysis, design of experiments, longitudinal analysis, surrogate models, transformers, Knowledge Graphs, Agents, Graph NNs, Deep Learning, computer visionAbility to write production code and object-oriented programming ...

computer science, or equivalent experience with experience in research Proven experience applying machine learning techniques to solve business problems Proven experience in translating technical methods to non-technical stakeholders Strong programming experience in Python and the relevant analytics libraries (e.g., pandas, numpy, matplotlib, scikit-learn, statsmodels, pymc, pytorch/…/keras, langchain). R and C++ optional. Experience with version control (GitHub) ML experience with causality, Bayesian statistics & optimization, survival analysis, design of experiments, longitudinal analysis, surrogate models, transformers, Knowledge Graphs, Agents, Graph NNs, Deep Learning, computer vision Ability to write production code and object-oriented ...

velocity, and tackle complex scenarios where traditional A/B tests fall short. You will build robust metric systems, apply Bayesian methods and multi-arm bandits, and mentor junior data scientists while ensuring products scale safely across markets and time. #J-18808-Ljbffr ...

Build production-grade Python packages Follow and build project scaffolds (PowerShell), follow coding standards, and reproducible workflows Transform analyses into standardized currently best-known methods (cBKM) Process data and automate data flows; manage and optimize data structure & data bases for big data sets Interact with product and service teams … Bachelor or Master Degree: Statistics/Computer Science/Data Science//Industrial Engineering Expert knowledge of Python, statistical modelling (frequentist and Bayesian) and data visualization Intermediate knowledge of PowerShell, R and SQL AI expert knowledge Project management Comfortable with non-recurrent, high-complexity engineering analyses ...

and strategic level. Experience delivering marketing analytics e.g., Marketing Mix Modeling , geo experiments, meta-analysis, audience segmentation and propensity modeling . Understanding of Bayesian approaches and modeling frameworks. Proven ability to generate practical solutions for marketing analytics problems and use results to drive business change in partnership … talent. Responsibilities Provide support in media strategy, measurement and optimization that require expertise in advanced analytics work, with special focus on marketing analytics methods and data science. Partner with internal teams in advanced analytics work including experimentation, measurement and modeling. Identify patterns and behaviors that are effective predictors ...

data and audiences we build for. Comfortable with sport-driven modelling decisions. Machine Learning & Statistics: Solid grounding in machine learning, supervised and unsupervised methods, and classical statistical techniques. Comfortable working with probabilistic models, uncertainty estimation and Bayesian inference. Model Development: Understanding of the full model training … Engineering: Experience building and maintaining production data pipelines, or working with AWS services such as Lambda, EventBridge and DynamoDB. Simulation: Experience with Monte Carlo methods or probabilistic simulation. AI Integration: Comfortable using AI-assisted coding tools such as Claude Code or Cursor as part of your everyday workflow, and ...

mission. You’ll excel if You have experience in any of: control systems, time-series modelling, 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, physics, chemistry, engineering, mathematics or computer science. You have a track record of developing novel ML methods in relevant fields. You are hungry for responsibility, enthusiastic about taking on the design and development of solutions to difficult problems, and eager ...

quality, customer effort, process adherence, consistency, fairness, durability of resolution and cost effectiveness. Identify the root causes of issues by combining machine learning, statistical methods and domain expertise from colleagues to understand what genuinely drives customer outcomes. Identify and test solutions Turn analytical insight into concrete proposals to improve … Python skills, with experience working with large, complex and imperfect datasets. Strong grounding in statistical analysis (e.g., classical ANOVA, non-parametric uncertainty quantification, Bayesian estimation) and causal inference, with experience designing both controlled experiments and observational studies. Experience applying machine learning, statistical modelling, NLP and LLM-based ...

team working at the intersection of artificial intelligence and quantum sensing. The positions will focus on the development and deployment of real-time AI methods for the autonomous optimisation of spin-based quantum sensors associated to solid-state defects. The successful candidates will join a research environment that … and conferences, and/or other appropriate media. Other duties Understand the intellectual property landscape relevant to the project and seek to protect the methods and techniques developed during the course of the project. Develop research objectives and proposals for own or joint research with the assistance ...

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 ...

linking/equating and research studies, supports the completion of studies by others, and develops white papers and research summaries.Implements and improves psychometric research methods and processes at MetaMetrics according to best practices, industry standards, and academic literature.Maintains awareness of psychometric literature and methods regarding test and delivery … methods (e.g., fixed-form, computer adaptive, and linear-on-the-fly), statistical methods (e.g., simulation studies), and computational psychometrics (e.g., machine learning, Bayesian inference).Provides ad hoc or planned expert consultation to clients and their stakeholders (e.g., technical advisory committees) regarding methods, results, and ...

/equating and research studies, supports the completion of studies by others, and develops white papers and research summaries. Implements and improves psychometric research methods and processes at MetaMetrics according to best practices, industry standards, and academic literature. Maintains awareness of psychometric literature and methods regarding test and … delivery methods (e.g., fixed-form, computer adaptive, and linear-on-the-fly), statistical methods (e.g., simulation studies), and computational psychometrics (e.g., machine learning, Bayesian inference). Provides ad hoc or planned expert consultation to clients and their stakeholders (e.g., technical advisory committees) regarding methods ...