Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Employer
- University of Oslo
- Forschungszentrum Jülich
- ISGLOBAL
- Technical University of Denmark
- CNRS
- Cornell University
- Ghent University
- King Abdullah University of Science and Technology
- Technical University of Munich
- University of Amsterdam (UvA)
- University of Glasgow
- University of Lund
- University of Minnesota
- University of Sheffield
- Auburn University
- BRGM
- Binghamton University
- Cambridge, University of
- Cardiff University
- Centrale Supelec
- Chalmers University of Technology
- Curtin University
- Eindhoven University of Technology
- Eindhoven University of Technology (TU/e)
- Fermilab
- Florida Atlantic University
- Harbin Engineering University
- INRIA
- Indiana University
- Institut Pasteur
- KINGS COLLEGE LONDON
- LAUM UMR CNRS 6613
- Laboratoire de Génie CHimique
- Lehigh University
- Lunds universitet
- Northeastern University
- Queen's University Belfast
- Radboud University
- SciLifeLab
- Swedish University of Agricultural Sciences
- The University of Alabama
- Tilburg University
- UNIVERSITE DE TECHNOLOGIE DE COMPIEGNE
- UNamur - Lab of F. De Laender
- Umeå universitet
- University College Dublin
- University of Bologna
- University of British Columbia
- University of Idaho
- University of Manchester
- University of Nottingham
- University of Texas at Arlington
- University of Utah
- University of Washington
- Université Clermont Auvergne
- Université de Caen Normandie
- Uppsala universitet
- Utrecht University
- 48 more »
- « less
-
Field
-
Your Job: This PhD project develops a Bayesian inference framework for hybrid model- and data-driven modeling of metabolism, with a particular focus on handling model misspecification. By combining
-
Bayesian Index Tracking: optimisation by sampling School of Mathematical and Physical Sciences PhD Research Project Self Funded Dr Kostas Triantafyllopoulos, Dr Dimitrios Roxanas Application
-
entitled “Beyond Data-Augmentation: Advancing Bayesian Inference for Stochastic Disease Transmission Models”. The overarching aim of the project is to develop the next generation of statistical tools
-
Sklodowska-Curie Doctoral Network linking 21 academic, cultural, and industrial partners to develop advanced nondestructive evaluation and data-driven digital tools for paintings and 3D artworks (https
-
generative models, methods for approximate inference, probabilistic programming, Bayesian deep learning, causal inference, reinforcement learning, graph neural networks, and geometric deep learning. Want
-
conduct research in several areas: analysis of high-dimensional data, Bayesian methods, spatial-temporal models, non-Gaussian modeling, applied research in social science, as well as stochastic models and
-
staff. We conduct research in several areas: analysis of high-dimensional data, Bayesian methods, spatial-temporal models, non-Gaussian modeling, applied research in social science, as well as stochastic
-
induced seismicity. Current models remain limited by the scarcity, heterogeneity, and noise of available data, as well as by incomplete knowledge of the subsurface. Physics-Informed Neural Networks (PINNs
-
. ------------------------------------------------------------------------------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------------------------------------...
-
, methodologies, and information derived from Bayesian modeling, data science, cognitive science, and risk analysis. Its primary objective is to create advanced forecasting models, generate meaningful indicators