Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- Argonne
- ;
- Heriot Watt University
- King Abdullah University of Science and Technology
- Technical University of Munich
- University of Oxford
- Brookhaven Lab
- European Space Agency
- Forschungszentrum Jülich
- Leibniz
- Los Alamos National Laboratory
- Manchester Metropolitan University
- Massachusetts Institute of Technology (MIT)
- Nature Careers
- Nottingham Trent University
- Oak Ridge National Laboratory
- Rutgers University
- Technical University of Denmark
- The Ohio State University
- Umeå University
- University of California Berkeley
- University of Delaware
- University of Lund
- University of Minnesota
- University of Tübingen
- University of Virginia
- 16 more »
- « less
-
Field
-
• Uncertainty quantification around LLMs • Constrained optimal experimental design (active learning) • Combining models and combining data / Realistic simulation of clinical trials • Developing
-
a testbed of micromorphic numerical models, and metamaterials. Proposing experimental methods to obtain micromorphic models under small and large strain, with coupled uncertainty quantification
-
a testbed of micromorphic numerical models, and metamaterials. Proposing experimental methods to obtain micromorphic models under small and large strain, with coupled uncertainty quantification
-
simulation methods, decision theory, uncertainty quantification, machine learning. Applications and areas of key innovation Image analysis, computer graphics, autonomous and assisted driving, 3D scene analysis
-
Research profile in machine learning (e.g. robustness, out-of-distribution/anomaly detection, fairness, explainability, uncertainty quantification) or AI applications in the healthcare domain Interest in
-
learning, small data learning · Active learning, Bayesian deep learning, uncertainty quantification · Graph neural networks This position involves active participation in a well-funded
-
to increasing CO2 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
-
-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
-
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
-
. Use of radiation transport codes, especially MCNP, Serpent, OpenMC, or an equivalent code. Experience with uncertainty quantification methods. Experience with computer programming (Python, C