Are you a highly motivated researcher and excited by interdisciplinary research on developing new methods for analysing complex dynamic network systems using uncertain data? Apply for a PhD position in the Data Analytics and Digitalisation department at Maastricht University. You will develop methods to reconstruct time-evolving networks from uncertain and indirect observational data and apply these to real-world complex systems.
Complex systems play an important role in many aspects of our lives, including technological systems such as the world wide web, telecommunications and power grids, biological systems of metabolic interactions, neuronal activity of the brain, as well as the way we interact in society. Key to understanding these complex systems is the use of networks that allow us to analyse the system as a whole, rather than as a collection of independent units. Most empirical studies of networks, as well as the methods they employ, assume that the network data we are given represent a complete and accurate picture of the nodes and edges in the system of interest. However, data collected on real-world systems are typically prone to noise, errors, omissions and inconsistencies. This project aims to better understand the impact of these uncertainties on the analysis of time-evolving complex systems and develop statistical models and inference methods that are robust to noisy, error-prone or missing data.
You will develop models of uncertainty to study the effects of noise and missing data on temporal network analysis. The aims of the project are to (i) develop methods to reconstruct networks from noisy and indirect observations of dynamic complex systems, (ii) determine the limits of network reconstruction in uncertain data, and (iii) apply these methods to real-word systems.
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