101 to 125 of 128 Permanent Bayesian Methods Jobs

/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 ...

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 ...

website). Key responsibilities Develop your own research program, with a view to developing new tools and techniques for probabilistic models, Bayesian optimization and related fields. Engage in team collaborations to meet research goals and report your research findings both internally and externally. Contribute to internal product … advantage: A background in decision making (Bayesian optimization, bandits, reinforcement learning, active learning) and/or probabilistic modelling and methods (Gaussian processes, Bayesian neural networks, Variational inference, etc). Experience in numerical programming (Python/NumPy/Tensorflow/PyTorch). Experience ...

exploration, training, deployment, monitoring. Non-linear methods. You can think in terms of the cost function and inductive biases of your models Bayesian methods. You have used hierarchical models, MCMC, or Bayesian updating in real work. Time series modelling. You have modelled temporal data ...

uncertainty quantification, experimental design, causal inference and Bayesian modeling. Just as essential is commercial instinct: the judgment to design rigorous, workable methods within real product goals and constraints, built through meaningful, hands-on experience shipping at startup speed. You will report to the CTO and work … product, and customer context. You need complete information before acting, rather than exercising judgment under uncertainty and evolving guidance. You'd rather follow established methods than create structure from ambiguity; defining and refining our evaluation process is core to the job. You're uncomfortable owning the quality bar and ...

array of statistical genetics approaches (e.g., GWAS, colocalization, fine-mapping, Mendelian randomization, polygenic risk scores)Experience defining phenotypes from electronic health records.Knowledge of Bayesian inference, high dimensional statistics, causal inference.Experience with software engineering practices (version control [Git & GitHub], testing, documentation, agentic coding, containerisation)Experience … building and running reproducible pipelines e.g., using WDL, Snakemake, or NextFlow.Experience using cloud computing and trusted research environments e.g. DNAnexus or Verily Workbench.Experience with methods development within statistical geneticsYour PackageWe are committed to providing a transparent, supportive, and rewarding work environment.Compensation & GrowthCompetitive Salary: Salaries are externally benchmarked annually ...

statistical genetics approaches (e.g., GWAS, colocalization, fine-mapping, Mendelian randomization, polygenic risk scores)Experience defining phenotypes from electronic health records. Knowledge of Bayesian inference, high dimensional statistics, causal inference. Experience with software engineering practices (version control [Git & GitHub], testing, documentation, agentic coding, containerisation)Experience building and … running reproducible pipelines e.g., using WDL, Snakemake, or NextFlow. Experience using cloud computing and trusted research environments e.g. DNAnexus or Verily Workbench. Experience with methods development within statistical geneticsYour PackageWe are committed to providing a transparent, supportive, and rewarding work environment. Compensation & GrowthCompetitive Salary: Salaries are externally benchmarked annually ...

automation. The 45% is the distinguishing feature: a candidate who can run the programme but cannot challenge a metric definition or assess a Bayesian approach will deliver roughly half of what the role requires. You'll collaborate with Product Analytics, Analytics Engineering, Engineering leaders and Trade partners … Product, Engineering, Analytics, Trade and Data to keep experimentation aligned to company goals, communicating vision and outcomes clearly across the business. Champion quasi-experimental methods (e.g. diff-in-diff, synthetic control, matched market tests) for scenarios where randomised testing isn't feasible, and build a playbook to guide their ...

Develop an independent research program focused on new tools and techniques for probabilistic models, Bayesian optimization, and related fields Collaborate with research teams to meet research goals Report research findings internally and externally Contribute to internal product development and customer research projects Apply cutting-edge machine learning … journals Background in decision making, including Bayesian optimization, bandits, reinforcement learning, or active learning (advantage) Background in probabilistic modelling and methods, including Gaussian processes, Bayesian neural networks, or variational inference (advantage) Experience in numerical programming with Python, NumPy, TensorFlow, or PyTorch (advantage) Experience ...

risk, lending, or customer lifetime value modelling. Non-linear methods. You have shipped gradient boosting or neural networks on tabular data in production. Bayesian methods. You have used hierarchical models, MCMC, or Bayesian updating in real work. Time series modelling. You have modelled temporal ...

hormonal rhythms observed across individuals. To characterise this variability, you will take advantage of unique in its kind datasets and advanced mathematical and computational methods and help develop personalised models. The practical element of your project will be based on, but not limited to, time series analysis, network analysis … being welcoming and supportive. The applicant should have high proficiency with scientific computer programming in Python, Julia, R and/or MATLAB, parameter estimation methods and HPC simulations. Experience with dynamical systems theory and mathematical modelling using ODEs and/or DDEs is desirable. The vacancy ...

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 qubits associated to solid-state defects. We expect the candidate to supervise on-going work on an existing … 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 ...

delivery teams within the UK Defence or Aerospace sectors. Experience in the practical application of mathematical techniques such as Monte Carlo simulation or Bayesian inference. Experience working with MoD assessment frameworks and an understanding of the UK Defence Science and Technology (S&T) landscape. Experience conducting peer … production environment. Experience working within secure environments and an understanding of the constraints of working with classified information. Expert knowledge of numerical methods, optimisation, and statistical analysis within a defence context Proficiency in mathematical programming using Python (NumPy, SciPy), FORTRAN, or Julia. Familiarity with simulation tools and synthetic environments ...

bonus Understanding of model cost functions and inductive biases is a bonus Experience with hierarchical models, MCMC, or Bayesian updating is a bonus Experience modelling temporal data involving autocorrelation, drift, or seasonality is a bonus Python experience is a bonus Core Competencies Demonstrates expertise in credit risk … Risk Modelling Customer Lifetime Value Modelling Machine Learning Model Development Causal Estimation Python Programming Hard Skills Probability Statistics Model Evaluation Hierarchical Models MCMC Bayesian Updating Temporal Data Modelling Inductive Biases Automation Research Methodology Soft Skills Critical Thinking Communication Problem Solving Analytical Mindset Collaboration Industry Keywords Lending Credit ...

harness massive compute power and to use state-of-the-art ML techniques – published in recent conferences or developed entirely in-house – as textbook methods won’t beat the competition.ML is integral to develop successful investment management strategies; it is one of the core drivers of our overall performance … learning, non-convex optimisation, Bayesian non-parametrics, NLP or approximate inferenceExcellent reasoning skills and mathematical ability are crucial: off-the-shelf methods don’t always work on our data so you will need to understand how to develop your own modelsStrong programming skills and experience working ...

data transformations using distributed computing (e.g., Spark, Dask) and ensure robust test coverage for production systems. Mentor junior team members and utilise model explainability methods to drive feature performance improvements. About You5+ years of experience in designing, building and maintaining machine learning models to solve complex, large-scale business … problems. Deep understanding of frequentist and Bayesian statistics, plus supervised and unsupervised modelling techniques. Proven experience modelling complex interactions (e.g., cluster or network effects).Expertise in model explainability to tune feature engineering and understand feature interactions. Ability to write high-quality, production-grade Python code and collaborate ...

data transformations using distributed computing (e.g., Spark, Dask) and ensure robust test coverage for production systems. Mentor junior team members and utilise model explainability methods to drive feature performance improvements. About You 5+ years of experience in designing, building and maintaining machine learning models to solve complex, large-scale … business problems. Deep understanding of frequentist and Bayesian statistics, plus supervised and unsupervised modelling techniques. Proven experience modelling complex interactions (e.g., cluster or network effects). Expertise in model explainability to tune feature engineering and understand feature interactions. Ability to write high-quality, production-grade Python code ...

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 ...

Postgraduate Research Student (funded). DL June 30, 2022 I am hiring researchers to my team working on probabilistic machine learning. Keywords include: Bayesian inference, reinforcement learning and inverse reinforcement learning, automatic experimental design, multi-agent learning, Bayesian deep learning, amortized inference, human ...

seeking a Robotics Software Engineer for our Online Calibration team to support scaling across our autonomous vehicle fleet. You will turn calibration and estimation methods into reliable, observable and resource-efficient components of the on-vehicle runtime, operating continuously on constrained, hardware-accelerated platforms. Working across calibration, state estimation … closely related robotics domain, including calibration of cameras, LiDAR, IMUs, vehicle parameters or multi-sensor systems. Strong understanding of filtering and estimation methods, such as Kalman-family filters, Bayesian estimation, smoothing, robust estimation or factor-graph optimisation. Strong modern C++ software-engineering skills and proficiency ...

and develop capabilities while mentoring junior statisticians and building client relationships. The role requires strong expertise in Bayesian statistics, population-adjustment methods (MAIC/ML-NMR), ITCs, and survival analyses, with extensive R and Stan/BUGS programming experience. #J-18808-Ljbffr ...

each stage of the consumer’s lifecycle. Experience overseeing experimentation programmes and practices that inform product or business decisions. A good understanding of methods for statistical inference (e.g. Bayesian, Frequentist) and familiarity with predictive analytics techniques is highly beneficial. Working knowledge of common analytics tools used ...

Research Associate in Bayesian Non Parametric at Imperial College London Jul 20, 2018 Research Associate in Statistics Applications are invited for a Research Associate position in the Department of Mathematics at Imperial College London to work in the area of Bayesian Non-Parametric statistics. … Sarah Filippi and in collaboration with Prof Chris Holmes (Alan Turing Institute). The post holder will work on developing a novel Bayesian Non-Parametric Test for Conditional Independence. This is at the core of modern causal discovery, itself of paramount importance throughout the sciences and ...

novel safety evaluations in high-impact domains such as CBRN and Cyber to quantify emerging risks. Executing original technical research in AI safety evaluation methods, taking ideas from concept to publication. Shaping the R&D agenda by identifying strategic opportunities to advance safety evaluation methodology across Faculty and … jailbreaking, indirect prompt injection, and assessing the robustness of model safeguards. Strong foundational skills in experimental design, statistical analysis, and uncertainty quantification, including Bayesian methods. Deep knowledge of language models, generative AI architectures, training methodologies, and safety mitigation techniques. Solid Python proficiency combined with the engineering discipline ...

and supervising junior researchers. You will contribute to real-time adaptive quantum sensing work using NV centres, develop Bayesian/inference methods, and #J-18808-Ljbffr ...