Progressive visual analytics of collective behaviour data
2 Ph.D/Postdoctoral Positions
Research objectives: The focus of this project is the visual exploration and analysis of the vast amounts of complex heterogeneous data that are generated in our experiments. To foster understanding, and to gain new insights, automated data analysis and machine learning techniques are indispensable, but they need to be complemented with interactive visual data exploration techniques in order to allow domain experts to “inject” their background knowledge into the process and to fine-tune and adapt necessary computations for the analysis tasks at hand. In particular, progressive (incremental) algorithms for the analysis of spatio-temporal, network, and multi-dimensional data will be developed that have to be coupled with progressive visualisation methods. We will also automatically detect users’ interests based on their actions and gaze and use these to trigger potentially interesting analyses by background processes. Advanced, large and high-resolution displays, and for some data immersive, visual user environments, will be developed to allow us interacting with the data.
The Data Analysis and Visualization Group in the Department of Computer Science seeks one PhD/Post-Doctoral candidate to develop visual exploration and analysis interfaces for the vast amounts of heterogeneous data generated in various projects of the excellence cluster. Candidates should have a background in data analysis and/or information visualization and should be interested in applying it to explore and analyze collective behavior data. Successful candidates must have advanced programming skills as well as a Master or Ph.D. in Computer Science (Advisor: Daniel Keim )
The Life Science Informatics Group in the Department of Computer and Information Science is looking for one strong candidate for a PhD / PostDoc position to develop novel and innovative approaches that support the analysis and exploration of data sets generated in the excellence cluster in close interdisciplinary collaboration with other cluster researchers. A focus will be on modelling, analysis and visualisation of networks as well as on adapting immersive analytics approaches. The developed algorithms and methods should help to understand the interplay between communication and movement dynamics in animal groups, to analyse large-scale movement data such as delivered by the ICARUS system, to study the principles underlying the relationship between network structure and processes in collectives and to explore the role of heterogeneity between and within groups. Candidates thus should have a strong background in at least one of the following areas: network analysis, network visualisation and immersive analytics. Good algorithm engineering and programming skills as well as a Master or Ph.D. degree are expected. (Advisor: Falk Schreiber)