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
- Nature Careers
- New York University
- Rutgers University
- The Ohio State University
- University of Florida
- University of Idaho
- University of Oslo
- Aalborg University
- Arizona State University
- Boston University
- Cornell 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
- Oak Ridge National Laboratory
- SciLifeLab
- Stanford University
- Swedish University of Agricultural Sciences
- Technical University of Denmark
- The University of Arizona
- UNIVERSITY OF VIENNA
- University of British Columbia
- University of Copenhagen
- University of London
- University of Luxembourg
- University of Minnesota Twin Cities
- University of Oxford
- University of Virginia
- Virginia Tech
- 31 more »
- « less
-
Field
-
project on Bayesian comparisons between artificial and natural representations to improve our understanding how natural and artificial intelligences process information. The project is led by Heiko Schütt
-
techniques from statistical physics, Bayesian inference, and complex systems theory to address challenges posed by noisy and incomplete data. You will contribute to method development, simulation and
-
detection framework for tipping points. Contribute to the design of scalable and interpretable forecasting strategies for large climate simulators, integrating adaptive sampling and Bayesian techniques
-
with a strong background in molecular virology, next-generation sequencing, Bayesian analysis, phylogenetic analysis, statistical genetics, and the ability to use R and/or UNIX/command line applications
-
Vitelli. Successful candidates will work on Bayesian models for unsupervised learning when multiple data sources are available, mostly tailored to the case of molecular data in cancer genomics. The position
-
environmental conditions under various hydrologic restoration scenarios. ELVeS is a flexible modeling framework for exploration of non-normal plant distribution responses to environmental variables. A Bayesian
-
related to gravitational wave astronomy. The primary aim will be the development of advanced approaches for computational Bayesian Inference to measure the properties of Compact Binary Coalescence signals
-
functional data ”, led by Associate Professor Valeria Vitelli. Successful candidates will work on Bayesian models for unsupervised learning when multiple data sources are available, mostly tailored to the case
-
performance. The salary is commensurate with experience. Applications are invited from individuals who are interested in applying experimental psychology and Bayesian computational modeling to understanding
-
for this position include: (1) Experience with human behavioral and/or neuroimaging experiments. (2) A strong technical background in Bayesian and reinforcement learning models. Please apply with your CV. For people