Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
-
. Desirable Expertise in computational fluid mechanics, broadly construed. Expertise in Bayesian methodology for optimization and experiment design. Experience with equivariant neural networks. Track record
-
constituting a large national and international network of potential collaborators. Where to apply Website https://emploi.cnrs.fr/Candidat/Offre/UMR7164-SANMER-033/Candidater.aspx Requirements Research
-
experience would include most topics in modern statistics and topics like Bayesian Machine Learning and Simulation Based Inference (a past research focus on neural network architectures is not a prerequisite
-
– Advanced Research Training for Additive Manufacturing of the Biomaterials and Tissues of the Future https://cordis.europa.eu/project/id/101226431 . This network has 8 host institutions hiring doctoral
-
, and organizational networks, we characterize the dynamics of knowledge creation and application. Current projects include both the production and evolution of scientific knowledge and the application
-
intelligence, machine learning, big data and network analysis, computational and Bayesian methods, are encouraged to apply. Minimum Qualifications PhD in Statistics or closely related fields with documented
-
” website: https://www.rsm.nl/faculty-research/departments/strategic-management-and-entrepreneurship/phd-in-strategic-management/ Opens external Keywords Entrepreneurship, Innovation, Strategy Renewal
-
Bayesian framework and two specific proposed lines of research: (1) constructing suitable priors via neural networks approximations, and (2) enhancing the sensitivity and efficiency of posterior diagnostics
-
, including working with multivariate data. Substantial experience in code development and, preferably, experience with Bayesian statistical modelling techniques and software. Willing to develop expert
-
, computer science, data science, or related fields. The candidates are expected to have a profound knowledge on the majority of the following topics: Machine learning (deep neural networks) Bayesian machine learning