Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- University of Minnesota
- ;
- Duke University
- Heriot Watt University
- King Abdullah University of Science and Technology
- Technical University of Denmark
- Aarhus University
- Argonne
- Arizona State University
- Aston University
- CEA
- Chalmers University of Technology
- DURHAM UNIVERSITY
- Durham University
- Florida International University
- Forschungszentrum Jülich
- Fraunhofer-Gesellschaft
- Georgetown University
- KINGS COLLEGE LONDON
- Max Planck Institute for Multidisciplinary Sciences, Göttingen
- McGill University
- Nature Careers
- New York University
- Oak Ridge National Laboratory
- Purdue University
- Radboud University
- Rutgers University
- SciLifeLab
- Stanford University
- Swedish University of Agricultural Sciences
- The University of Arizona
- UNIVERSITY OF VIENNA
- University of Adelaide
- University of British Columbia
- University of Copenhagen
- University of Idaho
- University of London
- University of Luxembourg
- University of Minnesota Twin Cities
- University of Oslo
- University of Oxford
- University of Virginia
- Virginia Tech
- 33 more »
- « less
-
Field
-
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
-
statistical analysis and modeling techniques such as Gaussian process modeling, data assimilation, and Bayesian analysis; and 4. Open-source scientific software development. Expertise in computational
-
regressions, Bayesian analyses). You preferably have experience supervising and/or teaching students. You preferably have knowledge of swarm robotics and/or deep learning artificial neural networks (e.g. CNN
-
projects ranging from score-based generative models, energy-based models, Bayesian analysis of graph and network structured data, highly multivariate stochastic processes; with data applications ranging from
-
experiments. The objective is to develop Bayesian causal models and neural networks capable of identifying relevant causal relationships between instrumental parameters and observed anomalies. The work will
-
-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
-
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 | 5 days 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
-
-technical audiences and engage in stakeholder or end-user consultation. DESIRED CHARACTERISTICS: Demonstrated experience in models of opinion dynamics, Bayesian reasoning models, natural language processing
-
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