Sort by
Refine Your Search
-
Listed
-
Country
-
Program
-
Employer
- Chalmers University of Technology
- AALTO UNIVERSITY
- European Space Agency
- Oak Ridge National Laboratory
- University of Nottingham
- University of Oslo
- Biophysical and Biomedical Measurement Group - NIST
- CNRS
- Delft University of Technology (TU Delft)
- Forschungszentrum Jülich
- Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association
- King Abdullah University of Science and Technology
- Nanyang Technological University
- National Aeronautics and Space Administration (NASA)
- New York University
- REQUIMTE - Rede de Quimica e Tecnologia
- SINGAPORE INSTITUTE OF TECHNOLOGY (SIT)
- SUNY University at Buffalo
- UNIVERSITAT POMPEU FABRA
- UNIVERSITE DE TECHNOLOGIE DE COMPIEGNE
- University of Maryland Baltimore County
- University of Michigan - Ann Arbor
- University of Oxford
- University of Texas at Austin
- University of Washington
- Università degli Studi di Brescia
- Yale University
- Zintellect
- 18 more »
- « less
-
Field
-
National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 5 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
-
. To this end, domain decomposition techniques, uncertainty quantification, and reduced models—possibly based on neural network training—will be considered, along with the development of theoretical results
-
Biophysical and Biomedical Measurement Group - NIST | Gaithersburg, Maryland | United States | 13 days ago
processing, internal standards/calibration, and uncertainty quantification. - Experience with spectroscopy, chromatography, electrophoresis, fluorescence methods, or complementary molecular characterization
-
Qualifications Experience with graph neural networks, machine-learning interatomic potentials, or related scientific machine-learning methods for atomistic systems. Familiarity with uncertainty quantification
-
on frontier models like Large Language Models (LLMs) and multimodal foundation models. This includes topics such as safety alignment, adversarial training, jailbreaking, uncertainty quantification, and AI
-
states are unobserved. Purely data-driven models offer flexibility, but often ignore known biology and provide limited insight into uncertainty and mechanisms. These challenges motivate a broader Biology
-
. · Quantification and Propagation of uncertainty in industrial environments (noisy sensors, sensor degradation, evolving production processes, rare events, incomplete datasets…) o quantifying epistemic and
-
to high-dimensional statistics; Bayesian statistics; resampling techniques; digital twins; uncertainty quantification; foundations of machine learning and artificial intelligence; optimization theory and
-
, recurrent memory, Bayesian modelling, uncertainty quantification and machine learning systems. Emphasis will be on methods that design and implement new architectures for (auto-regressive) sequence modelling
-
Description The main tasks to be carried out by the selected candidate will be the following: ● Develop new methods for the uncertainty quantification of non-linear statistical models using the 'online