We have an exciting new PhD position in visual analytics of multi-layer networks The Visual Data Analysis research group at KU Leuven (University of Leuven) is part of the department of Biosystems at the Faculty of Bioscience Engineering. The group focusses on complex data exploration in the domain of biological and agricultural sciences, through the use of visual analytics and topological data analysis. The group is currently looking for a dynamic and highly motivated PhD student. Project Networks and graphs - both static and dynamic - play an important role in data science. Not only can they represent primary relational data (e.g. gene interactions) but many algorithms generate networks either as intermediate or final output (e.g. DBSCAN clustering and topological data analysis). In particular, these data structures are critical when investigating (biological and other) systems as a whole. We can quantitatively compare networks at different resolutions, and distinguish between changes in topology and changes in characteristics of the nodes and links themselves. Although many measures exist to do so (including degree, closeness, betweenness, etc), they mainly focus on the amount of difference but fall short in giving real insight in the quality of that difference. We want to focus on this qualitative understanding of networks rather than only a quantitative one. We want to develop a human-in-the-loop visual analytics toolkit to support the user in exploring differences between two or more networks in depth. Visual Analytics (VA) is often described as the science of analytical reasoning facilitated by the visual interface and combines interactive data visualisation and novel visual design on one hand with machine learning on the other. The methodology will involve - among other things - custom visual design, definition and implementation of interestingness features, as well as definition of novel distance metrics for topological data analysis. This will be applied to different types of multi-layer networks (e.g. with/without ordering in 2 or more dimensions). Keywords: graph, network, topological data analysis, visual analytics, data visualisation, multilayer networks Example papers on the topic of multi-layer networks and topological data analysis: * McGee et al. The State of the Art in Multilayer Network Visualization. Computer Graphics Forum 38, no. 6 (2019): 125–49. https://doi.org/10.1111/cgf.13610 * Lum et al. Extracting Insights from the Shape of Complex Data Using Topology. Scientific Reports 3 (2013): 1236. https://doi.org/10.1038/srep01236 Profile The Ideal Candidate * has a Master's degree in Computer Science, Bioscience Engineering or similar, with distinction (required) * has a solid understanding of network analysis / graph theory and a strong foundation in general data visualisation principles and techniques * has experience in topological data analysis (TDA) * has good programming and data analysis skills (incl using Python and if possible Javascript) * can work independently as well as part of a collaborative cross-domain research team * is proficient in oral and written English, possess excellent communication and multi-tasking skills, is team-oriented, proactive and result-driven. Interested Candidates Should Submit The Following * curriculum vitae * digital copy of the diplomas, including a list of courses followed and grades * contact information of 2 or 3 referees * motivation letter, which specifies why they apply to this position, and clearly illustrate possible past experience in the area. Offer * A fully funded, full-time PhD position for one year; after a positive evaluation, the contract can be extended to three additional years (four years in total) * A working climate where trust, (international) collaboration, and commitment are essential * An excellent young, stimulating and supportive international research environment * High level scientific training at a top-ranked university; training in academic, thematic and soft skills. How to apply For more information on this opportunity, please contact Prof Jan Aerts, tel.: +32 16 32 21 40, mail: jan.aerts@kuleuven.be You can apply via the KU Leuven jobsite at https://www.kuleuven.be/personeel/jobsite/jobs/60288125 Deadline for submitting: 11/1/2024 --- Prof Jan Aerts Visual Data Analysis Lab Department of Biosystems KU Leuven, Belgium jan.aerts@kuleuven.be https://www.biw.kuleuven.be/biosyst ; http://vda-lab.io