Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- CNRS
- KINGS COLLEGE LONDON
- University of Washington
- Aarhus University
- Empa
- Heriot Watt University
- King Abdullah University of Science and Technology
- Technical University of Denmark
- UNIVERSITY OF HELSINKI
- University of Oxford
- ;
- ASNR
- Argonne
- Arizona State University
- Aston University
- CEA
- Chalmers University of Technology
- City University London
- Columbia University
- Duke University
- Eindhoven University of Technology (TU/e)
- Embry-Riddle Aeronautical University
- Florida International University
- Friedrich Schiller University Jena
- Georgetown University
- Japan Agency for Marine-Earth Science and Technology
- KTH Royal Institute of Technology
- King's College London
- Max Planck Institute for Multidisciplinary Sciences, Göttingen
- Nature Careers
- Oak Ridge National Laboratory
- Purdue University
- Rutgers University
- Stony Brook University
- The Ohio State University
- UNIVERSITY OF VIENNA
- University of Adelaide
- University of Central Florida
- University of Colorado
- University of Idaho
- University of London
- University of Maine
- University of Minnesota
- University of Oslo
- University of Sydney
- University of Virginia
- Université de Caen Normandie
- Virginia Tech
- 38 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
-
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
-
cryo-EM equipped with a Summit K2 direct electron detector, BioQuantumenergy filter and a Volta phase plate; Aquilos 2 cryo-FIB/SEM; Leica DM6 FS/EM cryo-CLEM system; NMR facility (Bruker 800 MHz and 700
-
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
-
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