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
- Heriot Watt University
- Imperial College London;
- National Centre for Nuclear Research
- Rice 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 California, Berkeley
- University of Exeter;
- University of Miami
- University of Nebraska–Lincoln
- University of Texas at Austin
- University of Warsaw
- University of Washington
- Université d'Orléans
- Zintellect
- 24 more »
- « less
-
Field
-
software engineering, Bayesian modeling and approaches to data analysis. Key Responsibilities: Preprocessing and data scientific approaches to analyzing human behavioral data Computational model development
-
annotation using bespoke references, and downstream perturbation-level and gene-level effect estimation, as well as the development of sophisticated approaches and biologically grounded perturbation prediction
-
-dimensional niche models, and applying advanced Bayesian spatio-temporal methods. You will: Build n-dimensional abiotic niches for >6,700 species and estimate population positions within them. Quantify niche
-
there are innumerable examples of its application, one important observation is the low proportion of studies proposing the estimation of uncertainties (<5%). Yet uncertainties can be multiple and of different natures
-
Inria, the French national research institute for the digital sciences | Saint Martin, Midi Pyrenees | France | 29 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