The emerging discipline of Computational Social Science (CSS) studies human behaviour, as manifested in the digital trails we leave in our interactions with each other. The development of mathematical models for CSS is urgently required to underpin the analysis of large-scale data and to move beyond the identification of correlations to create new scientific understanding of collective behaviour in both online and offline social networks.
In this Science Foundation Ireland funded project, we are seeking to recruit two postdoctoral researchers to develop new mathematical techniques and models to help revolutionise the understanding of the dynamics of social spreading phenomena, such as viral information contagion and cascades of popularity. We will focus on the mathematics of age-dependent (non-Markovian) branching processes to generate analytical and asymptotic results for inference and calibration with large-scale CSS data. Understanding and controlling the temporal aspects of information diffusion and cascade dynamics on social networks will improve the predictability of technology adoption and opinion propagation and enable us to accurately identify the most influential nodes within diverse dynamical systems on complex networks. As part of the project, we will seek to develop and apply algorithms to social spreading phenomena that are of interest to industry partners.
The Mathematics Applications Consortium for Science and Industry (MACSI) is Ireland’s largest applied and industrial mathematics group and works closely with scientists and industrial companies across a wide variety of sectors. MACSI’s aim is to foster new collaborative research, in particular on problems that arise in industry through the application of cutting-edge mathematical and modelling techniques.
The candidate should have completed a doctoral degree in applied mathematics, mathematics, physics, computer science, or other relevant discipline, with a strong publication record in either or both of the following areas:
- Mathematics of networks.
- Analysis and model calibration using large-scale Computational Social Science data.
Further details of the posts are available here. Please note that applications must be submitted online at www.ul.ie/hrvacancies (search using keyword MACSI) in advance of 12 noon Irish Standard Time on 31 October 2017.