<html>
<head>
<meta http-equiv="content-type" content="text/html; charset=UTF-8">
</head>
<body>
<p>Dear all,</p>
<p>I am happy to announce a funded <b>PhD position on
neuro-symbolic modelling of music</b> at Durham University (UK).
The project is about combining <b>deep learning</b> with
structured/<b>symbolic methods</b> (artificial grammars, graphical
models, etc.) to address fundamental challenges in <b>machine
learning </b>and<b> music analysis</b>.<br>
</p>
More details can be found <a moz-do-not-send="true"
href="https://euraxess.ec.europa.eu/jobs/787162">here</a> and
below. Please distribute to anyone who might be interested.
<p>Many thanks!</p>
<p>Best wishes,<br>
Robert</p>
<p><i>(apologies for cross-posting)</i></p>
<p>-- <br>
<span style="font-family: Sans-Serif;"> <b>Dr Robert Lieck</b>
(he/him)<br>
<b>Assistant Professor</b><br>
<i>Department of Computer Science</i><br>
<i>Durham University</i><br>
<a href="mailto:robert.lieck@durham.ac.uk"
class="moz-txt-link-freetext" moz-do-not-send="true">robert.lieck@durham.ac.uk</a>
</span></p>
<p>========================================<br>
</p>
<p><b>More information can be found </b><b><a
moz-do-not-send="true"
href="https://euraxess.ec.europa.eu/jobs/787162">here</a></b><b>.</b><br>
</p>
<p>This funded PhD position is about developing novel algorithmic
tools for music analysis using deep learning and
structured/symbolic methods. It will combine approaches from
computational musicology, image analysis, and natural language
processing to advance the state of the art in the field.<br>
<br>
Music analysis is a highly challenging task for which artificial
intelligence (AI) and machine learning (ML) is lagging far behind
the capabilities of human experts. Solving it requires a
combination of two different model types: (1) neural networks and
deep learning techniques to extract features from the input data
and (2) structured graphical models and artificial grammars to
represent the complex dependencies in a musical piece. The central
goal of the project is to leverage the synergies from combining
these techniques to build models that achieve human-expert level
performance in analysing the structure of a musical piece.</p>
<p><b>You will get:</b><br>
</p>
<ul>
<li>the chance to do your PhD at a <strong>world-class university</strong>
and conduct <strong>groundbreaking research </strong>in
machine learning and artificial intelligence</li>
<li>the opportunity to work on an <strong>interdisciplinary </strong>project
with <strong>real-world applications</strong> in the field of
music</li>
<li><strong>committed supervision</strong> and <strong>comprehensive
training</strong> (regular one-on-one meetings, ample time for
discussion, detailed feedback, support in your scientific
development, e.g., presentation skills, research methodology,
scientific writing etc.)</li>
<li>a <strong>stimulating</strong>, <strong>diverse</strong>,
and <strong>supportive </strong>research environment (as
member of the interdisciplinary <a
href="https://aihs.webspace.durham.ac.uk/"
moz-do-not-send="true">AIHS group</a>)</li>
<li>the opportunity to publish in <strong>top journals</strong>,
attend <strong>international conferences</strong>, and build a
<strong>network of collaborations</strong></li>
</ul>
<p><b>You should bring:</b></p>
<ul>
<li><strong>enthusiasm </strong>for interdisciplinary research in
artificial intelligence and music</li>
<li>an <strong>open mind-set</strong> and creative <strong>problem-solving</strong>
skills</li>
<li>a <strong>solution-oriented</strong> can-do mentality</li>
<li>a desire to <strong>understand </strong>the structure of <strong>music
</strong>and its inner workings</li>
<li>a good command of a<strong> modern programming language</strong>
(preferably Python) and familiarity with a modern <strong>deep
learning framework</strong> (e.g. PyTorch)</li>
<li>a strong master degree (or equivalent) with a significant <strong>mathematical
</strong>or <strong>computational </strong>component<br>
</li>
</ul>
<p>If you are interested, please send an email with your CV and a
short informal motivation to Robert Lieck <a
href="mailto:robert.lieck@durham.ac.uk"
class="moz-txt-link-freetext" moz-do-not-send="true">robert.lieck@durham.ac.uk</a>
for initial discussions.</p>
<p><b>Important Note:</b> We are looking to fill this position as
soon as possible (the position is still open as long as it is
advertised) and are accepting applications on a rolling basis. The
preferred start date is October 2022 (new academic year). We would
particularly like to encourage applications from women, disabled,
Black, Asian and other minority ethnic candidates, since these
groups are currently underrepresented in our area.</p>
</body>
</html>