Sort by
Refine Your Search
-
Listed
-
Country
-
Employer
- Oak Ridge National Laboratory
- Chalmers University of Technology
- Delft University of Technology (TU Delft)
- European Space Agency
- Argonne
- Duke University
- Empa
- King Abdullah University of Science and Technology
- Lawrence Berkeley National Laboratory
- Lehigh University
- National Aeronautics and Space Administration (NASA)
- Pennsylvania State University
- University of California
- Aarhus University
- Biophysical and Biomedical Measurement Group - NIST
- Forschungszentrum Jülich
- Harvard University
- Helmholtz Association of German Research Centres
- Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association
- Iowa State University
- King's College London
- Lancaster University
- National Energy Technology Laboratory (NETL)
- New York University
- Royal Holloway, University of London;
- SUNY University at Buffalo
- SciLifeLab
- University of Connecticut
- University of London
- University of Maryland Baltimore County
- University of Minnesota
- University of Oxford
- University of Washington
- Virginia Tech
- Yale University
- 25 more »
- « less
-
Field
-
the quantification and representation of uncertainty within and between these models. Addressing these questions is essential if their predictions are to be used for scientific understanding and societal decision
-
the surrogate forward models with a Bayesian inverse modeling framework to achieve real-time or near-real-time uncertainty quantification, such that we can efficiently resolve the uncertainties rising from rock
-
National Aeronautics and Space Administration (NASA) | Hampton, Virginia | United States | 13 days ago
-sensitive modeling, high-fidelity experimental characterization, and uncertainty quantification capabilities for metallic aerospace materials, with emphasis on additively manufactured (AM) metals
-
models can support energy-system modelling and optimization, including applications such as prediction, surrogate modelling, uncertainty quantification, and decision support. Coordinate the joint research
-
National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 13 days ago
and its spatial variability; use of precipitation coherence as a validation and uncertainty-quantification pathway for high-resolution soil moisture from NISAR and future L-band missions (e.g., ROSE-L
-
optimization models. Investigate how tabular foundation models can support energy-system modelling and optimization, including applications such as prediction, surrogate modelling, uncertainty quantification
-
contribute intellectually to new research directions within the SAGE Lab. Preferred Experience with multimodal sensing, computer vision, time-series analysis, uncertainty quantification, model calibration
-
candidate will join the Scientific Machine Learning group at TDB and SciLifeLab. The group develops theory, methods and software for data-driven science, with a current focus on uncertainty quantification
-
, and remote-sensing data) to support model benchmarking, parameterization, and uncertainty quantification. Explore and apply AI/ML approaches (e.g., machine-learning emulators, surrogate modeling, AI
-
, and remote-sensing data) to support model benchmarking, parameterization, and uncertainty quantification. Explore and apply AI/ML approaches (e.g., machine-learning emulators, surrogate modeling, AI