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The Visual Data Analysis Lab (<a href="http://vda-lab.io">http://vda-lab.io</a>) at the KU Leuven in Belgium invites applications for a full-funded
<u><b>PhD researcher in Visual Analytics applied to Multi-layer Networks</b></u>.
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<a rel="nofollow" href="https://www.kuleuven.be/personeel/jobsite/jobs/60288125" style="text-decoration: none" draggable="false"><font color="#272727" style="color: rgba(0, 0, 0, 0.847059);">PhD Position in Visual Analytics applied to Multilayer Networks</font></a></div>
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<div>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.</div>
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<div>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.</div>
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<div>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.</div>
<div>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).</div>
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<div>Keywords: graph, network, topological data analysis, visual analytics, data visualisation, multilayer networks</div>
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<div><u>Profile:</u></div>
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<div>The ideal candidate:</div>
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<li>has a Master's degree in Computer Science, Bioscience Engineering or similar, with distinction (required)</li><li>has a solid understanding of network analysis / graph theory and a strong foundation in general data visualisation principles and techniques</li><li>has experience in topological data analysis (TDA)</li><li>has good programming and data analysis skills (incl using Python and if possible Javascript)</li><li>can work independently as well as part of a collaborative cross-domain research team</li><li>is proficient in oral and written English, possess excellent communication and multi-tasking skills, is team-oriented, proactive and result-driven.</li></ul>
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<div><u>The offer:</u></div>
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<li>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)</li><li>A working climate where trust, (international) collaboration, and commitment are essential</li><li>An excellent young, stimulating and supportive international research environment</li><li>High level scientific training at a top-ranked university; training in academic, thematic and soft skills.</li></ul>
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<div><font color="#000000"><span style="caret-color: rgb(0, 0, 0);"><u>How to apply:</u></span></font></div>
<div><font color="#000000">To apply, please visit the university’s jobsite at </font>https://www.kuleuven.be/personeel/jobsite/jobs/60288125</div>
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Prof Jan Aerts</div>
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Visual Data Analysis Lab<br>
Department of Biosystems<br>
KU Leuven, Belgium<br>
jan.aerts@kuleuven.be<br>
https://www.biw.kuleuven.be/biosyst ; http://vda-lab.io<br>
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