Sort by
Refine Your Search
-
Listed
-
Country
-
Employer
- Monash University
- ETH Zurich
- University of Colorado
- University of Sheffield
- SUNY Polytechnic Institute
- Stony Brook University
- University of Glasgow
- Inria, the French national research institute for the digital sciences
- SciLifeLab
- University of North Carolina at Chapel Hill
- University of Toronto
- CNRS
- Heriot Watt University
- National Centre for Nuclear Research
- Rice University
- Rutgers University
- Simons Foundation/Flatiron Institute
- Technical University of Munich
- The University of Chicago
- Tilburg University
- Tilburg University; 16 Oct ’25 published
- University Paul Sabatier
- University of A Coruña
- University of Bristol
- University of British Columbia
- University of California San Francisco
- University of Miami
- University of Nebraska–Lincoln
- University of Texas at Austin
- University of Warsaw
- University of Washington
- Université d'Orléans
- Zintellect
- 23 more »
- « less
-
Field
-
foundations, combining ultrasonic guided wave monitoring, high-fidelity finite element simulations, Bayesian inference, and machine learning. Guided waves can propagate over long distances and reach areas
-
Inria, the French national research institute for the digital sciences | Saint Martin, Midi Pyrenees | France | 25 days ago
specifically, we use simulation-based inference (SBI) [1], a Bayesian approach that leverages deep generative models, such as conditional normalizing flows and score-diffusion models, to approximate
-
on proving conditions under which such algorithms are optimal, and develop mathematical bounds on their sub-optimality in more complex cases. 3) Numerical Solutions to Bayesian Optimal Stopping Problems
-
non stationnaires. Dans ces représentations (STFT/ spectro- gramme, ondelettes, etc.), les composantes d'intérêt apparaissent sous forme de ridges. Estimer ces ridges suffit alors à reconstruire les
-
of parametrization of these models based on least squares and Bayesian calibration techniques employing longitudinal series of anonymized PSA data from patients. 3) Analysis of the predictions, parameters, and
-
, simulations, and games, which use a variety of AI technologies to learn from, collaborate with, support, or improve humans; Deep Learning for Perception: Use of deep learning algorithms for computer vision
-
migration Developing appropriate statistical algorithms for updating model parameters estimates Working with database manager to organize the fish data and environmental covariates Analyzing data and
-
designs such as observational study, randomized clinical trial, adaptive randomizations, Bayesian analysis of randomized trials, conventional meta-analysis, meta-regression, and network meta-analysis Work
-
randomizations, Bayesian analysis of randomized trials, conventional meta-analysis, meta-regression, and network meta-analysis. · Develop as an educator by taking an active teaching role in POCUS and EBM
-
Inria, the French national research institute for the digital sciences | Saint Martin, Midi Pyrenees | France | 30 days ago
are available, from computer graphics, computer engineering, computational physics, biology and chemistry, and so on. When training data is produced from simulation codes, it can be generated along with