Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
-
Field
-
models and Bayesian approaches to tackle complex, real-world data? Join this PhD project to build dynamic models and study cognitive variability using ecological momentary assessment (EMA). Join us We are
-
., stochastic gradient methods and Bayesian learning), Probabilistic performance guarantees, leveraging tools from stochastic systems, RKHS-based learning, and Bayesian inference to certify performance and
-
optimization-based updates (e.g., stochastic gradient methods and Bayesian learning), Probabilistic performance guarantees, leveraging tools from stochastic systems, RKHS-based learning, and Bayesian inference
-
techniques including graph neural networks, Bayesian neural networks, conformal prediction intervals and generative AI for synthetic data generation. You will also develop frameworks for uncertainty
-
networks, Bayesian neural networks, conformal prediction intervals and generative AI for synthetic data generation. You will also develop frameworks for uncertainty quantification in forecasting and
-
. mixed effects regression models and/or Bayesian statistics; e.g., brms / lme4 packages). Excellent written and spoken English. Desirables (traits that would give you an advantage) Training in evolutionary
-
knowledge and/or practical experience in topics such as agent-based modelling, bayesian statistics, causal inference, data visualisation and graphical interfaces, geospatial data analysis, high-performance
-
observational data, and the application of advanced methods for longitudinal and prediction modelling. You will also conduct methodological research on Bayesian methods and other innovative methodology
-
, Bayesian modelling, or other formal models of decision-making and learning). Furthermore, your suitability is further supported by: a track record of publications in peer-reviewed journals; the ability
-
generative models, methods for approximate inference, probabilistic programming, Bayesian deep learning, causal inference, reinforcement learning, graph neural networks, and geometric deep learning. Want