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<div><font color="#000000"><span style="font-size: 14px;">Dear colleagues,</span></font></div>
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<div><font color="#000000"><span style="font-size: 14px;">We are excited to announce the opening of a new <b>PhD position</b> at the Visual Data Analysis Lab at KU Leuven (Belgium), on the topic of <b>Visual Analytics of Multilayer Networks</b>.</span></font></div>
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<div><font color="#000000"><span style="font-size: 14px;">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.</span></font></div>
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<div><font color="#000000"><span style="font-size: 14px;"><u>About the project</u></span></font></div>
<div><font color="#000000"><span style="font-size: 14px;">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).</span></font></div>
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<div><font color="#000000"><span style="font-size: 14px;">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.</span></font></div>
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<div><font color="#000000"><span style="font-size: 14px;">We want to develop a human-in-the-loop visual analytics toolkit to support the user in exploring differences between two or more networks. 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.</span></font></div>
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<div><font color="#000000"><span style="font-size: 14px;">The methodology will involve - among other things - requirement elicitation, custom visual design, definition and implementation of interestingness features, as well as definition of novel distance metrics
for topological data analysis.</span></font></div>
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<div><font color="#000000"><span style="font-size: 14px;">Keywords: graph, network, topological data analysis, visual analytics, data visualisation, multilayer networks</span></font></div>
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<div><font color="#000000"><span style="font-size: 14px;"><u>Candidate profile</u></span></font></div>
<div><font color="#000000"><span style="font-size: 14px;">The candidate should have a Master's degree in Computer Science, Bioscience Engineering or similar. In addition, they should have a solid understanding of network analysis and a strong foundation in
general data visualisation principles and techniques.</span></font></div>
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<div><font color="#000000"><span style="font-size: 14px;">They must have good programming skills (incl Python, and if possible Javascript) and the ability to work independently as well as part of a collaborative cross-domain research team. The candidate should
be proficient in oral and written English, possess excellent communication and multi-tasking skills, be team-oriented, proactive and result-driven.</span></font></div>
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<div><font color="#000000"><span style="font-size: 14px;">Interested candidates should submit their curriculum vitae, contact information of 2 or 3 referees, and a motivation letter. This motivation letter should specify why the candidate applies to this position,
and clearly illustrate possible past experience.</span></font></div>
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<div><font color="#000000"><span style="font-size: 14px;"><u>Our offer</u></span></font></div>
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<li><span style="caret-color: rgb(0, 0, 0); font-size: 14px; color: rgb(0, 0, 0);">A full-time PhD position for one year; after a positive evaluation, the contract can be extended to three additional years (four years in total)</span></li><li><span style="caret-color: rgb(0, 0, 0); font-size: 14px; color: rgb(0, 0, 0);">A working climate where trust, (international) collaboration, and commitment are essential</span></li><li><span style="caret-color: rgb(0, 0, 0); font-size: 14px; color: rgb(0, 0, 0);">An excellent young, stimulating and supportive international research environment</span></li><li><span style="caret-color: rgb(0, 0, 0); font-size: 14px; color: rgb(0, 0, 0);">High level scientific training at a top-ranked university; training in academic, thematic and soft skills.</span></li></ul>
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<div><font color="#000000"><span style="font-size: 14px;"><u>Interested?</u></span></font></div>
<div><font color="#000000"><span style="font-size: 14px;">For more information please contact Prof. dr. ir. Jan Aerts, tel.: +32 16 32 21 40, mail: <b>jan.aerts@kuleuven.be</b>.</span></font></div>
<div><font color="#000000"><span style="font-size: 14px;">You can apply for this job no later than November 15, 2023 at this URL: <b><u>https://www.kuleuven.be/personeel/jobsite/jobs/60260018</u></b></span></font></div>
<div><font color="#000000"><span style="font-size: 14px;">KU Leuven seeks to foster an environment where all talents can flourish, regardless of gender, age, cultural background, nationality or impairments. If you have any questions relating to accessibility
or support, please contact us at diversiteit.HR@kuleuven.be.</span></font></div>
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Prof Jan Aerts</div>
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Data Analysis in Digital Agriculture</div>
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Department of Biosystems</div>
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KU Leuven, Belgium</div>
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jan.aerts@kuleuven.be</div>
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https://www.biw.kuleuven.be/biosyst ; http://vda-lab.io</div>
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