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<p>Dear all,</p>
<p>I'm happy to announce a vacancy for a 5-year PhD position at
Utrecht University on the topic of Big data history of music.
Deadline for applying is 1 November 2020. For a full description
and online application form see <a class="moz-txt-link-freetext" href="https://www.uu.nl/en/organisation/working-at-utrecht-university/jobs/phd-position-in-big-data-history-of-music-10-fte" moz-do-not-send="true">https://www.uu.nl/en/organisation/working-at-utrecht-university/jobs/phd-position-in-big-data-history-of-music-10-fte</a>
. Please forward this message to whoever may be interested.<br>
</p>
<p>Best regards,</p>
<p>Frans Wiering<br>
</p>
<p><br>
</p>
<div class="content-block">
<h2 class="label-above">Job description</h2>
<p>The documented history of western music spans over a
millennium. Generations of scholars have written detailed
accounts of compositional techniques, musical styles and genres,
and influential composers and their masterworks. Hidden below
the great variety of western music are long-term patterns of
historical change, which are more difficult to study when using
traditional close reading methods of musicology. Computational
approaches analysing large amounts of musical works may help to
unearth these patterns.</p>
<p>During the 16th and 17th centuries a transition took place from
modality, with its emphasis on melodic structures, to harmonic
tonality governed by chord progressions. Musicologists still
struggle to understand this transition based on the study of
individual compositions. The project <em>Computational ANalysis</em>
<em>of TOnal STRuctures in EArly Music (CANTOSTREAM)</em>
proposes a big data approach instead. The project’s aim is to
create and use machine learning methods for ‘distant listening’
to a large cross-section of the available music, in order to a
gain deeper insight into the historical development of tonal
structures during these centuries.</p>
<p>As the prospective PhD candidate on this 5-year project, you
will be involved in studying the large body of early music that
has been recorded since the late 1960s. In addition, you will
analyse collections of encoded scores and large-scale metadata
resources such as RISM. Relevant features include modes, scales,
dissonance, cadences, melodic and harmonic patterns.</p>
<p>We offer a diverse set of tasks: </p>
<ul>
<li>assemble the musical corpus;</li>
<li>select and operationalise relevant musical concepts;</li>
<li>review, create and evaluate machine learning methods for the
analysis of musical corpora;</li>
<li>analyse historical change in relation to factors such as
geographical origin, genre, function and ensemble composition;</li>
<li>relate the outcomes to musicological insights and research
problems.</li>
</ul>
<p>In addition, you will partake in teaching Bachelor's and
Master's courses, offered by the Department of Information and
Computing Sciences. The teaching commitments are limited to a
maximum of 30% of employment time.</p>
</div>
<div class="content-block">
<h2 class="label-above">Qualifications</h2>
<p>We are looking for a candidate who is versatile and persistent
and who has:</p>
<ul>
<li>a Master’s degree in Computer Science, Artificial
Intelligence, Data Science, Information Science, Computational
Musicology or a related field;</li>
<li>well-developed programming skills;</li>
<li>a strong motivation for interdisciplinary research;</li>
<li>a working knowledge of music (notation, basic theory);</li>
<li>a passion for music history;</li>
<li>affinity for academic teaching;</li>
<li>good communication skills in English, both in speech and in
writing.</li>
</ul>
</div>
<div class="content-block">
<h2 class="label-above">Offer</h2>
<p>We offer an exciting opportunity to contribute to an ambitious
and international education programme with highly motivated
students and to conduct your own research project at a renowned
research university. You will receive appropriate training,
personal supervision, and guidance for both your research and
teaching tasks, which will provide an excellent start to an
academic career.<br>
In addition, you will have</p>
<ul>
<li>the opportunity to work in a collaborative, social, and
dedicated team of Researchers;</li>
<li>a full-time position for 5 years;</li>
<li>a full-time gross salary that starts at €2,395 and increases
to €3,061 per month in the fourth year (scale P of the
Collective Labour Agreement Dutch Universities (cao));</li>
<li>benefits including 8% holiday bonus and 8.3% end-of-year
bonus;</li>
<li>a pension scheme, partially paid parental leave, and
flexible employment conditions based on the Collective Labour
Agreement Dutch Universities.</li>
</ul>
<p>In addition to the employment conditions laid down in the cao
for Dutch Universities, Utrecht University has a number of its
own arrangements. For example, there are agreements on
professional development, leave arrangements and sports. We also
give you the opportunity to expand your terms of employment
yourself via the Employment Conditions Selection Model. This is
how we like to encourage you to continue to grow.</p>
</div>
<pre class="moz-signature" cols="72">--
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dr. Frans Wiering
Opleidingsdirecteur Informatiekunde
Associate Professor Interaction Technology
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Utrecht University
Department of Information and Computing Sciences (ICS)
Buys Ballot Building, office 482
Princetonplein 5
3584 CC Utrecht
Netherlands
mail: <a class="moz-txt-link-abbreviated" href="mailto:F.Wiering@uu.nl" moz-do-not-send="true">F.Wiering@uu.nl</a>
tel: +31-30-2536335
www: <a class="moz-txt-link-freetext" href="http://www.uu.nl/staff/FWiering/0" moz-do-not-send="true">http://www.uu.nl/staff/FWiering/0</a>
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