<html theme="default-dark" iconset="color"><head>
<meta http-equiv="content-type" content="text/html; charset=utf-8"></head><body
text="#000000">
Dear colleagues,<br>
<br>
The <span style="font-weight: bold; ">Interactive Visual Data Analysis
(IVDA) Group </span>at the <span style="font-weight: bold; ">University
of Zurich </span>(UZH) invites applications for both a <span
style="font-weight: bold; ">Ph.D. </span>and a <span style="font-weight:
bold; ">Post-Doc</span>
track (m/f/d), at the intersection of visual analytics, interactive ML,
and human-centered AI. Together, we will design and develop novel
approaches for the
characterization, design, and evaluation of interactive visual data
analysis solutions that combine the strengths of both humans and
algorithms. <br>
We are pursuing a human-centered approach to data science, machine
learning, and AI, to foster human involvement rather than replace it in
the data analysis process. In the context of digitalization, data-driven
research and decision-making, human-machine collaboration, and
human-centered AI, we will study human workflows, identify and preserve
steps with high human intellectuality, and establish practices of both
high human control and automation.<br>
<br>
<img src="cid:part1.11676E0B.B9F21527@ifi.uzh.ch" name="image.png"
alt="" style="width: 617px; height: 275px; "><br>
<br>
Both positions will work in a project about human preference elicitation
and multi-criteria decision support, which we will study for the case
of item ranking challenges. The figure exemplifies a multi-criteria
decision-making problem: buying the car, according to five elicited
criteria. We will study how such highly personalized ranking approaches
can be achieved interactively.<br>
<br>
<span style="font-weight: bold; ">Title</span> of the SNF-funded
project is: <span style="font-weight: bold; ">Personalized Visual
Analytics Human Preference Elicitation for Ranking-Based Multi-Criteria
Decision Support.<br>
</span><span style="font-weight: bold; "><span style="font-weight:
bold; ">Project duration</span></span> is four years, in collaboration
with Prof. Mennatallah El-Assady (ETH Zurich). <span style="font-weight:
bold; "><br>
</span><span style="font-weight: bold; "><span style="font-weight:
bold; ">Start date</span></span> is end of 2024.<span
style="font-weight: bold; "><br>
</span><span style="font-weight: bold; ">Flyer</span>:
<a class="moz-txt-link-freetext"
href="https://juergen-bernard.de/pdf/postDocFlyer.pdf" style="color:
rgb(0, 157, 247);">https://juergen-bernard.de/pdf/postDocFlyer.pdf</a><br>
<span style="font-weight: bold; ">J</span><span style="font-weight:
bold; ">ob Announcement</span>: at:
<a class="moz-txt-link-freetext"
href="https://www.ifi.uzh.ch/en/ivda/open-positions.html" style="color:
rgb(0, 157, 247);">https://www.ifi.uzh.ch/en/ivda/open-positions.html</a><br>
<br>
For more information, please reach out me.<br>
Best,<br>
Jürgen Bernard<br>
<br>
<span style="font-weight: bold; ">Research Context:</span>
Multi-criteria
decision support through item ranking is an understudied research
direction. Current solutions exhibit considerable limitations, primarily
due to their inability to meet the multi-faceted nature of human
decision-making. Choosing between thousands of relevant items is a
common task, where people are left alone with the need to express and
balance multiple desirable preferences at once. Item rankings are a
popular and universal approach to structuring unorganized item
collections by multiple criteria. With IVDA solutions, people can
express multiple preferences, used by algorithms to compute
human-centered and personal rankings. Visual Analytics (VA) is a field
of research that supports complex human decision-making tasks by
bringing together human intellectuality with the computational power of
algorithms in effective human-in-the-loop approaches. The figure on the
right exemplifies a multi-criteria decision-making problem: buying the
car, according to five elicited criteria. We will study how such highly
personalized ranking approaches can be achieved interactively.<br>
<br>
<div class="moz-signature">-- <br><span style="font-weight: bold;">Jürgen
Bernard</span><br>
Assistant Professor of Computer Science
<br>
University of Zurich
<br>
<span style="color: rgb(0, 106, 169);">juergen-bernard.</span><span
style="color: rgb(0, 106, 169);">info</span></div>
</body>
</html>