Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- Heriot Watt University
- Purdue University
- UNIVERSITY OF HELSINKI
- Aston University
- CEA
- CNRS
- Eindhoven University of Technology (TU/e)
- Friedrich Schiller University Jena
- Japan Agency for Marine-Earth Science and Technology
- Max Planck Institute for Multidisciplinary Sciences, Göttingen
- University of London
- 1 more »
- « less
-
Field
-
of the project. Apply established techniques and develop new methods inspired by Bayesian methods and statistical physics methodology for understanding the emergence of structure within cortical organoids and
-
-traditional, e.g., event data) and network structures (for sensor networks). In this project, we will investigate Bayesian deep learning approaches to training models under uncertainty for several sensing
-
surveillance) sensors can also be seen as temporal events. While data from current sensors can be manually converted into events for fast processing, it is also possible to develop hybrid structures where some
-
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