Networks for communication, transportation, finance and energy form the backbone of modern society. Reliable and efficient network infrastructures are therefore of enormous economic and social value, and their importance will only increase in the coming years. The complexity of networks and the processes running on them is increasing rapidly as well, which regularly leads to unpredictable and uncontrollable behaviour, and poses severe threats. Networks not only play a vital role for society, but they have also evolved into rich sources of data. These data are crucial for understanding structural properties and optimizing functional performance of large-scale networks. To deal effectively with the uncertainty, variation, unpredictability, size and complexity inherent in complex networks, we must develop radically new ways of thinking. The NETWORKS program uniquely combines stochastics (to model and understand large-scale networks and to predict network growth and network processes) with algorithmics (to control and optimize networks and network processes in the best possible way). The symbiosis of these two areas, one rooted in mathematics and the other in computer science, is what allows NETWORKS to make a decisive contribution to the advancement of network science. This symbiosis is underpinned by two paradigm shifts that have taken place in modern science: “randomness is everywhere” and “the need for fast algorithms”.
There is an urgent need for a generation of researchers who know how to deal with contemporary and emerging networks that are inherently stochastic in nature, and at the same time know how to design effective and well understood decision and optimization algorithms. The NETWORKS doctoral program will train 14 such researchers, who will gain the necessary scientific knowledge and professional skills for a successful career inside and outside academia, and who will contribute to the challenges posed by complex networks, by performing top-level research in an international environment. The main objectives of the NETWORKS doctoral program are:
- To bring together expertise from two different disciplines, namely stochastics (a subarea of mathematics) and algorithmics (a subarea of computer science), and train our ESRs by performing world-class research in, and at the interface between, these disciplines. Our multi-disciplinary approach is essential for complex network problems, since the performance of network algorithms is influenced by the random nature of network processes.
- To attract talented young researchers and offer them the best possible training and career perspectives. The extensive training program developed by NETWORKS includes scientific training in stochastics and algorithmics, and a broad range of key professional skills and personal development.
- To stimulate transnational and intersectoral mobility and knowledge transfer. NETWORKS will attract excellent young researchers from outside the Netherlands. Our large international network will provide them with excellent additional opportunities for transnational mobility. Intersectoral mobility and knowledge transfer are ensured through secondments at industrial and other non-academic partners of NETWORKS.
