Together, we will study different hypotheses, problems, and ideas concerning dynamic graph algorithms, in the pursuit of new, efficient algorithms.
Here, the fun challenge is to find just the right partial answers to maintain as the graph changes, and often, the road to efficient dynamic algorithms goes via new graph theoretic insights.
Contact: Eva Rotenberg.
IMFD Chile, http://www.imfd.cl offers an open position for a postdoc to advance the understanding of theoretical aspects of neural networks.
IMFD is a joint initiative held by several universities in Chile. It is a vibrant and truly interdisciplinary environment, which gathers together over 40 researchers and more than 100 students working on theoretical and applied aspects of data science.