Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- Newcastle University
- Technical University of Denmark
- University of Oslo
- Technical University of Munich
- 3rdplace
- BRGM
- CNRS
- Centrale Supelec
- Chalmers University of Technology
- Eindhoven University of Technology (TU/e)
- Forschungszentrum Jülich
- Fraunhofer-Gesellschaft
- INRIA
- Integreat -Norwegian Centre for Knowledge-driven Machine Learning
- Nature Careers
- SciLifeLab
- Swedish University of Agricultural Sciences
- UNamur - Lab of F. De Laender
- Umeå University
- University of Amsterdam (UvA)
- University of Bologna
- University of Surrey
- Université de Caen Normandie
- Uppsala universitet
- 14 more »
- « less
-
Field
-
. Bayesian networks and related machine-learning methods will be used to calculate cross-section probability density functions in a much faster way, enabling the combination of multiple probability
-
Your Job: This research primarily seeks to incorporate advanced neuron models, such as those capturing dendritic computation and probabilistic Bayesian network behavior, into unconventional
-
University of Oslo. Place of work is the Department of Biostatistics (OCBE), Domus Medica, Gaustad UiO campus, Oslo. Job description The position is connected to the project “Bayesian Enhanced Tensor
-
is part of the MET2ADAPT Doctoral Network (Meta-Materials and Meta-Structures for Adaptable, Resilient and Sustainable Renewable Energy Power Plants), a prestigious Marie Skłodowska-Curie Doctoral
-
programming, Bayesian deep learning, causal inference, reinforcement learning, graph neural networks, and geometric deep learning. In particular, you will be part of the Causality team under the supervision
-
insights that inform biodiversity management. The project includes: · Apply of deep learning models to annotate bird and bat species from sound recordings. · Develop a Bayesian statistical
-
Bayesian Networks (DBNs) for probabilistic risk modelling Scenario-based simulation for rare-event analysis You will be part of a dynamic, interdisciplinary research setting at one of Europe’s leading
-
the structure and robustness of the ecological networks supporting reef fish communities at different positions along depth, latitude, and longitude gradients; challenging these networks under hypothesized future
-
to make decisions for localization, navigation, and cooperation. Within the ERC Starting Grant project CUE-GO – Contextual Radio Cues for Enhancing Decision-Making in Networks of Autonomous Agents
-
. Is proficient in modern statistical modelling, AI & machine learning methods (e.g. system identification, regression models, Bayesian methods, deep learning). Is an experienced programmer in R and/or