Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
-
taken using different approaches such as Bayesian learning, evidential learning or conformal prediction to provide AI systems with mechanisms allowing them to “know when they do not know", provide
-
European Molecular Biology Laboratory (EMBL) | Brandenburg an der Havel, Brandenburg | Germany | 2 months ago
modelling, foundation models, cross-domain/-modality learning, explainable AI and mechanistic interpretability, representation learning, Bayesian inference, causal inference, active learning, AI-based agents
-
areas will be considered when selecting candidates: Machine Learning, Neural Networks, Numerical solutions of Partial Differential Equations and Stochastic Differential Equations, Numerical Optimization
-
in AI (machine learning, neural networks, reinforcement learning, dynamic modelling and/or Bayesian inference). You have experience in software development with solid knowledge of one or more
-
including, but not limited to, Dynamical Systems, Bifurcation Analysis, Complex Networks, Optimization, Mathematical Biology, Biostatistics, Mathematical Epidemiology, Computational Biology, Bioinformatics, P
-
opportunities for collaboration with Michigan State University, and IU’s network in cognitive modeling, AI, and human–AI decision research. This postdoctoral appointment is full-time and on-campus. Job Duties 80
-
, or multitrophic interactions is a strong merit. Experience with computational methods such as multilayer networks, Bayesian inference, or higher-order network models is also a merit. The ability to communicate
-
areas Biomedical applications, social determinants of health or other demographic health areas Spatial microsimulation, spatially weighted regression, combinatorial optimization or Bayesian network
-
systems, neuroscience, and safety and security. The Division of Systems and Control enjoys a wide network of strong international collaborators all around the world, for example at the University of Oxford
-
Elhoseiny, Code: https://github.com/yli1/CLCL Uncertainty-guided Continual Learning with Bayesian Neural Networks (ICLR’20), Sayna Ebrahimi, Mohamed Elhoseiny, Trevor Darrell, Marcus Rohrbach, Code: https