Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- CNRS
- KINGS COLLEGE LONDON
- Heriot Watt University
- King Abdullah University of Science and Technology
- Purdue University
- UNIVERSITY OF HELSINKI
- University of Washington
- Aarhus University
- Argonne
- Arizona State University
- Aston University
- Brookhaven Lab
- Brookhaven National Laboratory
- CEA
- Centro de Investigación en Matemáticas
- Dartmouth College
- Duke University
- Eindhoven University of Technology (TU/e)
- Florida International University
- French National Research Institute for Sustainable Development
- Friedrich Schiller University Jena
- Georgetown University
- Japan Agency for Marine-Earth Science and Technology
- King's College London
- King's College London;
- Massachusetts Institute of Technology
- Max Planck Institute for Multidisciplinary Sciences, Göttingen
- Nature Careers
- Technical University of Denmark
- The Ohio State University
- UNIVERSITY OF VIENNA
- University of Adelaide
- University of Colorado
- University of Florida
- University of Idaho
- University of London
- University of Maine
- University of Minnesota
- University of Oslo
- University of Oxford
- University of Sydney
- University of Virginia
- Virginia Tech
- 33 more »
- « less
-
Field
-
on the training strategies. In this project, we will investigate Bayesian methods to train deterministic SNNs (with deterministic activation functions) or probabilistic SNNs. Bayesian deep learning methods have
-
Max Planck Institute for Multidisciplinary Sciences, Göttingen | Gottingen, Niedersachsen | Germany | about 2 months ago
the structure from such data is challenging, and new theoretical methods and algorithms are required. The research project aims at deriving priors for Bayesian methods from atomistic simulations and machine
-
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
-
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
-
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
-
. Experience leading investigations linking simulations to observational data. Experience with statistical characterization of data, preferably within a Bayesian framework. Job Description: A Post-doctoral
-
applicants for a 6-month paternity leave replacement who have a strong interest in using computational methods such as cognitive and psychophysiological modeling, (Bayesian) statistics and optimal experimental
-
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
-
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
-
-technical audiences and engage in stakeholder or end-user consultation. DESIRED CHARACTERISTICS: Demonstrated experience in models of opinion dynamics, Bayesian reasoning models, natural language processing