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resilience. Deep understanding and implementation of advanced Kubernetes networking architectures, utilising CNI plugins, ingress controllers, and service meshes. Operating a GitOps-driven platform, enabling
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, from swarm robotics to mesh networks. The prototypical model system for the investigation of self-organised task allocation are social insect colonies, such as bees and ants. They are able to distribute
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estimation methods for deep neural networks. A principled Bayesian framework for multimodal uncertainty modeling. Robust learning algorithms under missing modalities and distribution shifts. New uncertainty
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at one time. In non-stationary environments on the other hand, the same algorithms cannot be applied as the underlying data distributions change constantly and the same models are not valid. Hence, we need
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strong out-of-distribution generalization capability [2]. If user-specific information is identified and removable from the input data, the devised techniques can also be applied for privacy-sensitive
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. This project concerns creating suitable models - and/or general hybrid models - as yet undeveloped. References: Comley, Joshua W. and D.L. Dowe (2003). General Bayesian Networks and Asymmetric Languages, Proc
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has been identified References: Comley, Joshua W. and D.L. Dowe (2003). General Bayesian Networks and Asymmetric Languages, Proc. 2nd Hawaii International Conference on Statistics and Related Fields, 5
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neural networks, we aim to improve the interpretability and robustness of reconstruction techniques. Another exciting direction involves self-supervised learning, which reduces reliance on fully labeled
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plants they visit and pollinate. Bayesian networks (BNs), and other probabilistic graphical models, can provide a visual representation of the underlying structure of a complex system by representing
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biological networks as a form of relational and structural learning. Given a network dataset, we wish to infer a model of the distribution of the elements of this data-set, possibly as a mixture of several