Sort by
Refine Your Search
-
Listed
-
Country
-
Employer
- Swedish University of Agricultural Sciences
- Technical University of Denmark
- Aarhus University
- Georgetown University
- King Abdullah University of Science and Technology
- Nature Careers
- Rutgers University
- SciLifeLab
- The University of Arizona
- University of Adelaide
- University of Copenhagen
- University of Idaho
- University of London
- University of Luxembourg
- University of Minnesota
- University of Oslo
- University of Virginia
- 7 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
-
We are looking for a postdoctoral researcher to work on prediction of local animal-plant networks. About the position A postdoctoral researcher position is available in Mariana Braga's group
-
focused on understanding and countering harmful narratives and, mis/disinformation, and applying social network analysis. To be successful you will need: PhD in a relevant discipline such as computer
-
development. Experience with implementing statistical learning or machine learning (e.g. Bayesian inference, deep-learning). Programming skills in Python and experience with frameworks like PyTorch, Keras, Pyro
-
fresh perspective on how specialized brain networks can identify and categorize causes of sensory inputs, integrate information with other networks, and adapt to new stimuli. It proposes that perception
-
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
-
learning, small data learning · Active learning, Bayesian deep learning, uncertainty quantification · Graph neural networks This position involves active participation in a well-funded
-
statistical approaches. A fundamental understanding of Deep Neural Networks as applied to high-frequency time series datasets, including the ability to design and implement custom NN models in PyTorch, as
-
of Denmark. The position is part of a larger EU project entitled “FEDORA - Federation of network optimisation services, simulation foresights, and data alchemy for adaptable, agile, secure, and resilient