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<th align="RIGHT" nowrap="nowrap" valign="BASELINE">Subject:
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<td>[Ieee_vis_open_positions] PhD position in Visual
Analytics for Paintings at the University of Amsterdam</td>
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<td>Wed, 04 May 2016 21:18:41 +0200</td>
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<th align="RIGHT" nowrap="nowrap" valign="BASELINE">From: </th>
<td>Marcel Worring <a class="moz-txt-link-rfc2396E" href="mailto:m.worring@uva.nl"><m.worring@uva.nl></a></td>
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<td>University of Amsterda</td>
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<th align="RIGHT" nowrap="nowrap" valign="BASELINE">To: </th>
<td><a class="moz-txt-link-abbreviated" href="mailto:ieee_vis_open_positions@listserv.uni-tuebingen.de">ieee_vis_open_positions@listserv.uni-tuebingen.de</a></td>
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<th align="RIGHT" nowrap="nowrap" valign="BASELINE">CC: </th>
<td>Worring, Marcel <a class="moz-txt-link-rfc2396E" href="mailto:M.Worring@uva.nl"><M.Worring@uva.nl></a></td>
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<b>PhD position in Visual Analytics for Paintings at the
University of Amsterdam</b><br>
<p>The aim of this project is to develop a visual analytics
framework in which art historians and automatic systems in a
synergetic manner create the stylistic history of large
collections of paintings in terms of visual characteristics,
place, and time. The technical challenge is to learn relevant
features from large collections of digital paintings,
interactively learn the similarity among the paintings and from
there arrive at a means of automatic visual story telling. For
more information see <a moz-do-not-send="true"
class="moz-txt-link-freetext"
href="http://www.uva.nl/en/about-the-uva/working-at-the-uva/vacancies/content/2016/04/16-194-phd-candidate-in-visual-analytics-for-paintings.html">http://www.uva.nl/en/about-the-uva/working-at-the-uva/vacancies/content/2016/04/16-194-phd-candidate-in-visual-analytics-for-paintings.html</a>.
<a moz-do-not-send="true"
href="https://staff.fnwi.uva.nl/m.worring/vistory.html"
target="_blank"><span class="external"></span></a></p>
<h4>Tasks for the PhD student<br>
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<li>Research and develop innovative visual analytics
methodologies integrating advanced computer vision, machine
learning, and information visualization techniques;</li>
<li>liaise with art historians from the UvA and imaging experts
from the Image Department of the Rijksmuseum to ingest domain
knowledge and assure impact;</li>
<li>evaluate the methods with art historians on large
collections of paintings. <br>
</li>
</ul>
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