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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.
The candidate will be responsible for developing novel probabilistic network models and using Bayesian inference to fit these models to real-word systems. The candidate should therefore:
Prior experience with Bayesian statistics and/or network science would be advantageous
Maastricht University is renowned for its unique, innovative, problem-based learning system, which is characterized by a small-scale and student-oriented approach. Research at UM is characterized by a multidisciplinary and thematic approach, and is concentrated in research institutes and schools. Maastricht University has around 20.000 students and 5.000 employees. Reflecting the university's strong international profile, a fair amount of both students and staff are from abroad. The university hosts six faculties: Faculty of Health, Medicine and Life Sciences, Faculty of Law, School of Business and Economics, Faculty of Science and Engineering, Faculty of Arts and Social Sciences, Faculty of Psychology and Neuroscience. For more information, see http://www.maastrichtuniversity.nl/
School of Business and Economics
SBE is the youngest economics and business faculty in the Netherlands with a distinctively international profile. It belongs to the 1% of business schools worldwide to be triple-crown accredited (EQUIS, AACSB and AMBA). SBE strongly believes in close connections with its academic partners and societal stakeholders, with its students and alumni, and with businesses and organisations in the Limburg Euregion, the Netherlands, Europe and the rest of the world. SBE is committed to increasing the gender diversity of its academic community and wants to stimulate the creation of a more diverse and inclusive community. For more information see: https://www.maastrichtuniversity.nl/about-um/faculties/school-business-and-economics
The department of Data Analytics and Digitalisation (DAD) connects data science (mathematics, statistics, computer science, artificial intelligence) with business and economics research (finance, accounting, marketing, information management, operations, micro- and macroeconomics, policy design). We are responsible for conducting top-level research in data science for business and economics, ranging from fundamental theoretical studies to applied industrial projects.
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