This Ph.D. position is part of a cross-theme project within the Institute of Informatics.
We seek a multi-disciplinary researcher who can bring advanced information-theoretic concepts, in particular synergistic information, from the field of complex adaptive systems into the field of deep learning and causal inference. The focus of this PhD project is on adapting and further developing the theory of synergy with the goal of making it viable for optimization. The end goal is to train deep representations that are robust and meaningful for un-/semi-supervised learning, transfer learning, and causal inference. For these purposes we anticipate that a ‘synergistic bottleneck principle’ needs to be formulated, in analogy to the ‘information bottleneck principle’. It should be worked out in terms of variational methods and applied to benchmark data sets. Furthermore, the question will be explored of how the optimization guided by synergistic information may serendipitously lead to causal representations.
This PhD project is a close collaboration between the Computational Science Lab (CSL, promotor prof. Peter Sloot) and the Amsterdam Machine Learning Lab (AMLAB, promotor prof. Max Welling).
For more details and how to apply please click here.