This Ph.D. position is part of a cross-theme project within the Institute of Informatics.

We seek a multidisciplinary researcher who can study deep neural networks in the context of complex adaptive systems analysis. Specifically, the aim is to reformulate and reinterpret a neural network description as an equivalent description of a dynamical system. Subsequently, using tools from dynamical systems and complex systems you will analyze the dynamical system and thereby gain insights into the original neural network. The goal is to gain insights into the structural and functional properties of the neural network computational graph resulting from the learning process. Techniques that will be employed include dynamical systems theory and iterative maps (chaotic attractors; Lyapunov exponent), information theory (Shannon entropy, mutual information, multivariate measures such as synergistic information (Quax et al., Entropy, 2018)), and network theory.

For more details and how to apply please click here.

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