<div dir="ltr"><div class="gmail_default" style="font-family:trebuchet ms,sans-serif;font-size:small"><span style="font-family:Calibri,sans-serif;font-size:12pt">Two
postdoctoral positions are available in the </span><a href="https://www.unive.it/pag/37559/" target="_blank" style="font-family:Calibri,sans-serif;font-size:12pt">Artificial Intelligence Unit</a><span style="font-family:Calibri,sans-serif;font-size:12pt"> of the
</span><a href="http://www.ecltech.org" target="_blank" style="font-family:Calibri,sans-serif;font-size:12pt">European Centre for Living Technology</a><span style="font-family:Calibri,sans-serif;font-size:12pt"> @ </span><a href="http://www.unive.it" target="_blank" style="font-family:Calibri,sans-serif;font-size:12pt">Ca’ Foscari University of Venice</a><span style="font-family:Calibri,sans-serif;font-size:12pt">,
Italy.</span><br></div><div class="gmail_quote"><div dir="ltr"><div style="font-family:trebuchet ms,sans-serif;font-size:small">
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<p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;font-size:12pt;font-family:"Calibri",sans-serif"><b><span lang="EN-US">1. Machine learning methods for knowledge graph exploration</span></b><b><span lang="EN-US">.</span></b><span lang="EN-US"> The focus is
the development of <span> </span>advanced machine
learning techniques based on the use of knowledge graphs in order to
automatically select and/or generate personalized content to cultural heritage
users. The work will be done within the EU-funded project MEMEX: MEMories and
EXperiences for inclusive digital storytelling.<span></span></span></p>
<p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;font-size:12pt;font-family:"Calibri",sans-serif"><span lang="EN-US"><span></span></span><span lang="EN-US"><span></span></span><span lang="EN-US"><span><br></span></span></p><p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;font-size:12pt;font-family:"Calibri",sans-serif"><b><span lang="EN-US">2. Countering evasion attacks in adversarial machine learning.</span></b><span lang="EN-US"> </span><span lang="EN-US"><span></span>The focus
is the development of <span></span>unsupervised and
semi-supervised machine learning models based on game theory and related
approaches to counter evasion attacks, namely attacks that manipulate input
data to evade a trained classifier at test time. The work will be done within
the MIUR-funded project REXlearn: Reliable and Explainable Adversarial Machine
Learning.<span></span></span></p>
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<p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;font-size:12pt;font-family:"Calibri",sans-serif"><span lang="EN-US"><b>Link:</b> <a href="https://www.unive.it/data/28825/" target="_blank">https://www.unive.it/data/28825/</a><span></span></span></p><span lang="EN-US"><span> </span><span></span></span>
<p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;font-size:12pt;font-family:"Calibri",sans-serif"><b><span lang="EN-US">Deadline: </span></b><span lang="EN-US">January 13, 2020</span><span lang="EN-US"> <span></span></span></p>
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<p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;font-size:12pt;font-family:"Calibri",sans-serif"><span lang="EN-US">The initial
appointments will be for one year, renewable on a yearly basis. <span></span></span></p>
<p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;font-size:12pt;font-family:"Calibri",sans-serif"><span lang="EN-US"><span><br></span></span></p><p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;font-size:12pt;font-family:"Calibri",sans-serif"><span lang="EN-US"><span><span style="font-size:12pt"><span style="font-family:Calibri,Arial,Helvetica,sans-serif"><span style="font-family:Calibri,Arial,Helvetica,sans-serif"><span style="font-family:Calibri,Arial,Helvetica,sans-serif">The
projects offer <span>competitive</span> <span>salaries</span> and the opportunity to work closely with high-profile members of the project consortia across Europe in the heart of one of the world's most fascinating cities.<br></span></span></span></span></span></span></p><p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;font-size:12pt;font-family:"Calibri",sans-serif"><span lang="EN-US"><span><span style="font-size:12pt"><span style="font-family:Calibri,Arial,Helvetica,sans-serif"><span style="font-family:Calibri,Arial,Helvetica,sans-serif"><span style="font-family:Calibri,Arial,Helvetica,sans-serif"></span></span></span></span> <br></span></span></p>
<p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;font-size:12pt;font-family:"Calibri",sans-serif"><span lang="EN-US">Candidates should
possess a Ph.D. degree in Computer Science or related disciplines. In addition,
they should have a good background in machine learning, and solid programming
skills. They must have the ability to work independently and with an
interdisciplinary team.<span></span></span></p>
<p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;font-size:12pt;font-family:"Calibri",sans-serif"><span lang="EN-US"><span><br></span></span></p><p class="MsoNormal" style="margin:0cm 0cm 0.0001pt;font-size:12pt;font-family:"Calibri",sans-serif"><span lang="EN-US"><span></span></span><span style="font-size:12pt;font-family:"Calibri",sans-serif" lang="EN-US">For more information about these positions,
please contact: <a href="mailto:pelillo@unive.it" target="_blank">pelillo@unive.it</a></span>
</p></div><br>-- <br><div dir="ltr" data-smartmail="gmail_signature"><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><span style="font-family:trebuchet ms,sans-serif"><span style="color:rgb(153,153,153)">Marcello Pelillo, <i>FIEEE, FIAPR</i><br>Professor of Computer Science</span></span></div><span style="font-family:trebuchet ms,sans-serif"><span style="color:rgb(153,153,153)">Ca' Foscari University of Venice, Italy</span></span><i><br></i></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div>
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