Bayesian Modeling Jobs in California (NOW HIRING)

AI Foundations areas that we are currently focusing on include reinforcement learning, learning from demonstration, generative modeling, Bayesian inference, hierarchical learning, and robust ...

Extensive data modeling and data architecture skills. * Advanced math skills (linear algebra, Bayesian statistics, group theory). * Ability to consistently exercise independent discretion and ...

AI Foundations areas that we are currently focusing on include reinforcement learning, learning from demonstration, generative modeling, Bayesian inference, hierarchical learning, and robust ...

Collaborates with stakeholders to define success criteria and align model metrics with business ... Deep knowledge of ML paradigms (supervised/unsupervised learning, reinforcement learning, Bayesian ...

Strong modeling and experimentation skills (Python, SQL, Bayesian / probabilistic modeling). * Comfort operating with limited data - capable of building and validating proxy-based models. * Blend of ...

AI Foundations areas that we are currently focusing on include reinforcement learning, learning from demonstration, generative modeling, Bayesian inference, hierarchical learning, and robust ...

AI Foundations areas that we are currently focusing on include reinforcement learning, learning from demonstration, generative modeling, Bayesian inference, hierarchical learning, and robust ...

Build and optimize state-space, Bayesian, and Kalman filter models for longitudinal, irregularly sampled, and multiscale physiological and digital phenotype data. * Develop interpretable multimodal ...

... Series, Bayesian Structural Time Series models, Seasonal-Trend Decomposition, and Rolling Regression; building and maintaining dashboards using Tableau/DOMO to integrate ETL processes ...

Proven experience with Bayesian statistics, applying methods such as Bayesian GBLUP, hierarchical models, and clustering using MCMC or variational inference. * Familiarity with decision theory and ...

Proven experience with Bayesian statistics, applying methods such as Bayesian GBLUP, hierarchical models, and clustering using MCMC or variational inference. * Familiarity with decision theory and ...

... Bayesian inference, mixed-effects models, ANOVA) Develop and validate perceptual simulation pipelines that predict visual appearance under varying display configurations, ambient conditions, and ...

... Series, Bayesian Structural Time Series models, Seasonal-Trend Decomposition, and Rolling Regression; building and maintaining dashboards using Tableau/DOMO to integrate ETL processes ...

... CMA-ES, Bayesian optimization). * Numerical analysis: numerical linear algebra (sparse/Krylov/preconditioning), stiff ODE/DAE solvers, approximation, quadrature; model reduction (POD/MOR)

Proven experience with Bayesian statistics, applying methods such as Bayesian GBLUP, hierarchical models, and clustering using MCMC or variational inference. * Familiarity with decision theory and ...

... modeling, Bayesian inference, surrogate/metamodeling, sensitivity analysis, uncertainty quantification, and beyond) to simulation output to produce decision-quality conclusions. • Build and own ...

... Bayesian inference, mixed-effects models, ANOVA) Develop and validate perceptual simulation pipelines that predict visual appearance under varying display configurations, ambient conditions, and ...

... CMA-ES, Bayesian optimization). * Numerical analysis: numerical linear algebra (sparse/Krylov/preconditioning), stiff ODE/DAE solvers, approximation, quadrature; model reduction (POD/MOR)

Critical aspects of the research include: (1) Designing and executing methods to integrate data from different sources, including developing a Bayesian Hierarchical Modeling framework; (2) using ...

Experience with Bayesian networks or probabilistic modeling. * Experience with uncertainty quantification techniques, such as Bayesian Inference, Monte Carlo simulation. * Experience with building ...