Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- CNRS
- University of Minnesota
- University of Washington
- Aarhus University
- Empa
- Heriot Watt University
- King Abdullah University of Science and Technology
- Rutgers University
- Technical University of Denmark
- 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
- Georgetown University
- KINGS COLLEGE LONDON
- Max Planck Institute for Multidisciplinary Sciences, Göttingen
- Nature Careers
- Oak Ridge National Laboratory
- Purdue University
- Stanford University
- Stony Brook University
- The Ohio State University
- UNIVERSITY OF VIENNA
- University of Adelaide
- University of Central Florida
- University of Colorado
- University of Florida
- University of Idaho
- University of London
- University of Maine
- University of Minnesota Twin Cities
- University of Oslo
- University of Southern California
- University of Sydney
- University of Virginia
- Virginia Tech
- 36 more »
- « less
-
Field
-
with statistical modeling (ideally Bayesian statistics) • Proficiency in Fortran, R, Python, Matlab, or ideally other common languages (e.g., C/C++) Strong computational skills Strong oral and written
-
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
-
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
-
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
-
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
-
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
-
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