Sort by
Refine Your Search
-
Listed
-
Country
-
Program
-
Employer
- Chalmers University of Technology
- European Space Agency
- Oak Ridge National Laboratory
- University of Nottingham
- University of Oslo
- AALTO UNIVERSITY
- Biophysical and Biomedical Measurement Group - NIST
- CNRS
- Delft University of Technology (TU Delft)
- Empa
- Forschungszentrum Jülich
- Ghent University
- 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
- SciLifeLab
- UNIVERSITAT POMPEU FABRA
- UNIVERSITE DE TECHNOLOGIE DE COMPIEGNE
- University of Maryland Baltimore County
- University of Michigan - Ann Arbor
- University of Minnesota
- University of North Carolina at Chapel Hill
- University of Oxford
- University of Texas at Austin
- University of Washington
- Università degli Studi di Brescia
- Yale University
- Zintellect
- 23 more »
- « less
-
Field
-
(PINNs) and surrogate modelling Time-series modelling and anomaly detection Bayesian methods and uncertainty quantification Graph Neural Networks (GNNs) Spatiotemporal data engineering Digital twins and
-
causal inference, integration of heterogeneous data sources, uncertainty quantification Work with a wide range of data types, for example dietary records, biomarkers, omics data, registry data, and sensor
-
Experience in one or more of the following areas: object detection and segmentation, multi-object tracking, time-series analysis, probabilistic modeling and uncertainty quantification, real-time or streaming
-
a postdoctoral researcher, you will: Integrate hybrid traffic models and data assimilation methods into a coherent prediction framework. Develop uncertainty quantification methods and explainable and
-
simulations, density functional theory (DFT), molecular simulations, or machine-learning potentials. Experience with generative AI, active learning, uncertainty quantification, Bayesian optimization
-
, Uncertainty quantification, Approximation Theory, Applied Probability and Bayesian statistics, Optimal Control and Dynamic Programming. Appointment, salary, and benefits. The appointment period is two years
-
. Experience applying machine learning to networking problems, for example, reinforcement learning, graph neural networks, or uncertainty quantification. A track record of publications in leading networking
-
. Experience applying machine learning to networking problems, for example, reinforcement learning, graph neural networks, or uncertainty quantification. A track record of publications in leading networking
-
, analyse, and validate innovative numerical algorithms and mathematical frameworks for problems arising in materials, fundamental physics, dynamics, optimisation, control, uncertainty quantification, inverse
-
, multidisciplinary team environment. Preferred Qualifications: Knowledge of uncertainty quantification methods and causal inference for complex environmental systems. Experience with large-scale Earth system