PhD positions on Knowledge Representation for Learning and Uncertainty

Application deadline: March 2017 (**see below for more information**)

The overall goal of these projects is to develop new methods and formal languages that can effectively bridge the areas of knowledge representation, probabilistic reasoning and machine learning. Formal languages and symbolic techniques have a long and distinguished history in AI, and have widely impacted many scientific and commercial endeavors in diverse areas such as verification, robotics, planning, logistics and human-level commonsense reasoning. However, many of the applications in these areas often need to handle inherent uncertainty, complemented by an increased prominence of data-oriented algorithms and statistical techniques. From a foundational perspective, the question of how knowledge representation languages need to be augmented to handle these complex notions of uncertainty is an open and challenging one. From a practical perspective, enriching existing machine learning algorithms by human-readable representations and background knowledge can be very useful. 

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