Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- ;
- University of Minnesota
- Aston University
- Chalmers University of Technology
- Duke University
- King Abdullah University of Science and Technology
- Technical University of Denmark
- University of Florida
- Arizona State University
- Boston University
- DURHAM UNIVERSITY
- Durham University
- Florida International University
- Forschungszentrum Jülich
- Fraunhofer-Gesellschaft
- Georgetown University
- Johns Hopkins University
- Max Planck Institute for Multidisciplinary Sciences, Göttingen
- McGill University
- Nature Careers
- New York University
- Oak Ridge National Laboratory
- Rutgers University
- SciLifeLab
- Stanford University
- Swedish University of Agricultural Sciences
- The University of Arizona
- UNIVERSITY OF VIENNA
- University of British Columbia
- University of Copenhagen
- University of Idaho
- University of London
- University of Luxembourg
- University of Minnesota Twin Cities
- University of Oslo
- University of Oxford
- University of Virginia
- Virginia Tech
- 28 more »
- « less
-
Field
-
linear ballistic accumulator models, diffusion models, biased competition models, or Bayesian models. During the employment, the candidate is expected to engage in the development of computational models
-
patients with cancer; to identify and validate predictive biomarkers of clinical outcomes in cancer; and perform meta- analyses using the Bayesian framework. The projects will lead to both collaborative and
-
on the development of Bayesian statistical/machine learning methods for the data integration analysis of high-throughput imaging and molecular data (i.e., genome, transcriptome, epigenome, and more). The methods would
-
at the intersection of systems neuroscience and computational modeling. Our lab is broadly interested in Bayesian inference, perception, multisensory integration, spatial navigation, sensorimotor loops, embodied
-
propagation problems, stochastic partial differential equations, geometric numerical integration, optimization, biomathematics, biostatistics, spatial modeling, Bayesian inference, high-dimensional data, large
-
Department of Ecology We are looking for a postdoc/researcher to develop and implement tools for analysis of output from Bayesian inference under phylogenetic models About the position A postdoc
-
(classic; Bayesian), machine learning, or other statistical approach with accompanying expertise in whatever stats package(s) is desired (SPSS; R; Stata; SAS; NumPy or PsyPy; etcetera). A strong ability to
-
methods is an asset Knowledge of Bayesian statistics is an asset Excellent written and oral communication skills Strong analytical, problem-solving, and programming skills (in statistical software such as R
-
(e.g., maximum parsimony; Bayesian inference), and utilizing these phylogenies to quantify evolutionary rates, directionality of trends, and patterns of morphological space occupation. You will have (or
-
Review, update, and consolidate methodologies, including Bayesian methodologies, in the context of material balance evaluation Your Profile: PhD in applied mathematics, computer science, physics, or in