Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
-
programming, Bayesian deep learning, causal inference, reinforcement learning, graph neural networks, and geometric deep learning. In particular, you will be part of the Causality team under the supervision
-
, 4) Model interpretability. Experience with other deep learning methods, such as Convolutional or Bayesian Neural Networks, Simulation-Based Inference (SBI), Normalizing Flows, or Diffusion Models, is
-
Your Job: This research primarily seeks to incorporate advanced neuron models, such as those capturing dendritic computation and probabilistic Bayesian network behavior, into unconventional
-
, please visit: https://qbm.genzentrum.lmu.de/application/ Tuition fees per semester in EUR None Combined Master's degree / PhD programme No Joint degree / double degree programme No Description/content
-
with statistical models incorporating random effects. A good working knowledge of Bayesian methods as well as Bayesian computation is desirable Proficiency in programming and designing statistical
-
Location: Ithaca, New York 14850, United States of America [map ] Subject Areas: Data Science / Statistics , Applied Mathematics , Artificial Intelligence , Bayesian Statistics , Big Data , Scientific
-
, e.g., by nationality (British Citizen) or 5+ years UK residency etc. Eligibility criteria and further information on the process can be found on the UK Government security vetting website, see https
-
valuable. Experience with population-level modeling approaches, including hierarchical or Bayesian modeling frameworks. Experience conducting research in Southeast Asia or comparable tropical field contexts
-
Job Description Do you want to figure out why Bayesian deep learning doesn’t work? And afterwards fix it? At DTU Compute we are working towards building highly scalable Bayesian approximations
-
beginning August 2026. Visit https://sc.fsu.edu , for more information. The successful candidate is expected to develop an interdisciplinary research group with a focus on Bayesian inference or inverse