Sort by
Refine Your Search
-
Listed
-
Country
-
Employer
- Monash University
- ETH Zurich
- Stony Brook University
- University of Glasgow
- Columbia University
- Nature Careers
- Rice University
- SUNY Polytechnic Institute
- SciLifeLab
- University of Colorado
- University of North Carolina at Chapel Hill
- University of Toronto
- University of Washington
- CNRS
- National Centre for Nuclear Research
- Oak Ridge National Laboratory
- Rutgers University
- Simons Foundation/Flatiron Institute
- Technical University of Munich
- Tilburg University
- Tilburg University; 16 Oct ’25 published
- University of A Coruña
- University of Bristol
- University of British Columbia
- University of California
- University of California San Francisco
- University of California, Berkeley
- University of California, San Diego
- University of Miami
- University of Nebraska–Lincoln
- University of Sheffield
- University of Texas at Austin
- University of Warsaw
- Université d'Orléans
- Zintellect
- 25 more »
- « less
-
Field
-
motif, hence renders the identification of the binding protein difficult. Here we propose for the first time to apply the Bayesian information-theoretic Minimum Message Length (MML) principle to optimise
-
sources, including developing a Bayesian Hierarchical Modeling framework; (2) using integrative modeling approaches to characterize heterogeneous protein assemblies structures and dynamics; (3) developing
-
-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
-
, 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
-
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
-
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
-
from, collaborate with, support, or improve humans; Deep Learning for Perception: Use of deep learning algorithms for computer vision, image and audio processing, and models of perception. The focus is