Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- Nature Careers
- NEW YORK UNIVERSITY ABU DHABI
- Argonne
- Technical University of Denmark
- Technical University of Munich
- University of Oxford
- ;
- Cornell University
- European Space Agency
- Stony Brook University
- Aalborg University
- Heriot Watt University
- King Abdullah University of Science and Technology
- Leibniz
- MOHAMMED VI POLYTECHNIC UNIVERSITY
- Stanford University
- Texas A&M University
- Umeå University
- Brookhaven Lab
- CEA
- CWI
- Centre for Genomic Regulation
- Chalmers University of Technology
- Florida International University
- Forschungszentrum Jülich
- Ghent University
- Linköping University
- Los Alamos National Laboratory
- Manchester Metropolitan University
- Max Planck Institute for Dynamics and Self-Organization, Göttingen
- National Renewable Energy Laboratory NREL
- Northeastern University
- Nottingham Trent University
- Oak Ridge National Laboratory
- Princeton University
- Rutgers University
- The Ohio State University
- The University of Arizona
- University College Cork
- University of California Berkeley
- University of Delaware
- University of Lund
- University of Minnesota
- University of Southern California
- University of Southern Denmark
- University of Tübingen
- University of Virginia
- Wageningen University and Research Center
- Yale University
- 39 more »
- « less
-
Field
-
and climatic change is a large uncertainty for ecosystems, crop productivity and climate predictions. To tackle this uncertainty, we combine: growth chamber experiments, samples from world-unique CO2
-
reducing the uncertainty of national greenhouse gas (GHG) inventories. Duties/Responsibilities 70% Primary Research Focus: This postdoctoral associate will apply advanced knowledge-guided AI-modeling
-
version of the model has also been drafted, which incorporates statistical rigor and explicit measures of uncertainty. With the goal of taking this version of the model to its completion, the successful
-
platforms. Experience in high-performance computing or working with large-scale simulation environments. Prior work involving model calibration, optimization under uncertainty, or scenario analysis
-
-temporal analysis, use of latent variable models such as VAEs, GANs, and diffusion models to capture complex distributions, methods for interpretability (e.g., SHAP values), as well as uncertainty
-
LBM implementation. Given that numerous measurements and parameters are subject to uncertainties, the project also incorporates uncertainty quantification (UQ) with the ultimate goal of providing
-
Projects: Identifies important nuclear-physics uncertainties for stellar models and, with local experimental and theoretical faculty and researchers, designs future studies to reduce those uncertainties. Why
-
the Digital Atlas, this project will be highly resolved in space and take novel approaches to modeling uncertainty. It will use and potentially expand a large collection of data compiled by Fabian Drixler and
-
. Implement conformal prediction and uncertainty quantification techniques to provide reliable risk assessments and uncertainty estimates in LLM applications. Present research findings at national and
-
. Moreover, the successful candidate will also need to develop a system to estimate the uncertainty of the predictions. Potential solutions could include ensemble generation, a combination of EOF