** FUNDED PHD POSITION AT TU VIENNA **
[Funded PhD Position] ViSual ANalytics for Event-based Diffusion on Networks @ TU Wien f/m/d (30-40h)
We are hiring a talented PhD candidate in an international project on the visual analytics of complex diffusion processes over temporal networks. You will work in the context of SANE, a project held by TU Vienna (Austria), the University of Cologne (Germany) and the University of Newcastle (United Kingdom). This project arrangement gives you unique career opportunities, including student exchanges abroad, regular meetings with researchers from the other institutions participating to the project.
We are looking for a PhD candidate to work from the TU Wien, at the Institute of Visual Computing and Human-Centered Technology, in the Research Unit of Visual Analytics. We offer a position as project assistant (prae-doc) limited to 3 years for 30 hours/week (that can beex-tended to 40). Gross annual (monthly) salary of Euro 43,650 (2,464.80) according to FWF regulations.
Pandemics, computer malware attacks, misinformation campaigns – all of these phenomena have one thing in common: some kind of “information” (a pathogen, a virus, fake news) that spreads across an underlying structure of elements interconnected between each other by some kind of relationship (physical contacts, public WiFi, social networks). In Computer Science, such structures are known as graphs or networks – and have been extensively researched. Within this formalization, the phenomena described above are referred to as diffusion processes, and their dynamics have been studied and researched to obtain models that allow researchers and institutions predict, mitigate, and generally understand such complex events. Visual Analytics (VA) showed its potential in communicating and investigating information diffusion processes over networks. Diffusion processes are highly dynamic and stochastic phenomena. To this end, VA needs to tackle two challenges: representing the progression on the underlying dynamic network structure and capturing the uncertainty of the process. The majority of existing VA approaches approximate the problem by imposing a discrete time structure to the input data and disregarding uncertainties.
We strive to be among the first to systematically explore the design space of VA for event-based information diffusion with uncertainty. We investigate promising but currently under-represented research trends in VA with innovative solutions and approaches, to contribute to the current body of knowledge in the field and open new and exciting research questions.
Wanna know more? Check the project description and complete job posting at https://www.cvast.tuwien.ac.at/projects/sane
Deadline for applications: 15 April 2024
Expected start: May/June 2024
Questions? You can reach me at a.arleo@tue.nl