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<p
style="margin-top:0pt;margin-bottom:6pt;font-family:Arial;font-size:11.0pt"
lang="en-US">WIAS invites applications for a </p>
<p
style="margin-top:6pt;margin-bottom:6pt;font-family:Arial;font-size:20.0pt"
lang="en-US"><span style="font-weight:bold">PhD student position
(f/m/d)</span></p>
<p
style="margin-top:0pt;margin-bottom:12pt;font-family:Arial;font-size:11.0pt"
lang="en-US"><span style="mso-spacerun:yes"> </span>(<span
style="font-weight:
bold">Ref. 22/17</span>)</p>
<p
style="margin-top:6pt;margin-bottom:6pt;font-family:Arial;font-size:11.0pt"
lang="en-US">in the Research Group</p>
<p
style="margin-top:6pt;margin-bottom:12pt;font-family:Arial;font-size:14.0pt"
lang="en-US"><span style="font-weight:bold">“Nonlinear
Optimization and Inverse
Problems”</span></p>
<p style="margin:0in;font-family:Arial" lang="en-US"><span
style="font-size:11.0pt">(Head:
Prof. Dr. D. Hömberg) </span><span
style="font-weight:bold;font-size:10.5pt">starting at
the earliest possible date.</span></p>
<p
style="margin-top:6pt;margin-bottom:6pt;font-family:Arial;font-size:11.0pt;color:black"
lang="en-US">The position is tied to the project</p>
<p
style="margin-top:6pt;margin-bottom:0pt;font-family:Arial;font-size:11.0pt;color:black"
lang="en-US"><span style="font-weight:bold">“Machine Learning for
Inverse Problems </span></p>
<p
style="margin-top:0pt;margin-bottom:6pt;font-family:Arial;font-size:11.0pt;color:black"
lang="en-US"><span style="font-weight:bold">with continuous
normalizing flows and mean field games”</span></p>
<p
style="margin-top:6pt;margin-bottom:6pt;font-family:Arial;font-size:11.0pt;color:black"
lang="en-US">PI: PD Dr. Martin Eigel). The goal of the project is
the development and analysis of Neural Networks for invertible
measure
transport such as normalizing flows. Connections to optimal
transport, optimal
control, mean field games and stochastic differential equations
will be
examined. Moreover, low-rank tensor formats will be used in a
hybrid method. In
collaboration with the PTB, the developed methods will be applied
to inverse
problems for geometry parameters the quality control of
semiconductor manufacturing.</p>
<p
style="margin-top:0pt;margin-bottom:6pt;font-family:Arial;font-size:11.0pt;color:black"
lang="en-US">We are looking for candidates with a solid background
in applied mathematics, theoretical chemistry, theoretical
physics, or
electrical engineering. They are expected to be familiar with some
of the
topics numerical analysis (for differential equations),
quantification of
uncertainty, statistical learning theory, high-dimensional
approximations,
stochastic analysis. Applicants are also expected to have
experience with at
least one of the popular Python frameworks for machine learning.
Previous
experience in continuum mechanics, thermodynamics, homogenization
theory,
software engineering, or machine learning are beneficial.</p>
<p
style="margin-top:0pt;margin-bottom:6pt;font-family:Arial;font-size:11.0pt;color:black"
lang="en-US">A completed scientific university degree (master’s
degree) in mathematics or a closely related field is required as
well as
demonstrable programming experience preferably in python and good
communication
skills in English. An applied mathematical education (in
particular numerical
analysis, functional analysis or stochastic analysis) is required
for a
successful application. Moreover, experience in the implementation
of machine
learning models and numerical algorithms is beneficial.</p>
<p
style="margin-top:6pt;margin-bottom:6pt;font-family:Arial;font-size:11.0pt"
lang="en-US">Queries about the project can be directed to Dr. M.
Eigel (<a href="mailto:eigel@wias-berlin.de?subject=22-03">Martin.Eigel@wias-berlin.de</a>).</p>
<p
style="margin-top:6pt;margin-bottom:6pt;font-family:Arial;font-size:11.0pt"><span
lang="en-US">The appointment is limited for three years until
31.03.2025. </span><span lang="de">The reduced work schedule is
29,25 hours per week, and the salary is
according to the German TVoeD Bund scale.</span></p>
<p
style="margin-top:0pt;margin-bottom:6pt;font-family:Arial;font-size:11.0pt"
lang="en-US">The Institute aims to increase the proportion of
women in this
field, so applications from women are particularly welcome. Among
equally
qualified applicants, disabled candidates will be given
preference.<span style="mso-spacerun:yes"> </span></p>
<p
style="margin-top:0pt;margin-bottom:6pt;font-family:Arial;font-size:11.0pt"
lang="en-US">Please upload your complete application documents
(motivation
letter, detailed CV, certificates, list of MSc courses and grades,
copy of the
master‘s thesis or draft, two recommendation contacts) via our <a
href="https://short.sg/j/17430658">applicant portal</a> as soon
as possible but
not later than <span style="font-weight:bold">May 15, 2022</span>
using
the button "<a href="https://short.sg/a/17430658">Apply online</a>".</p>
<p style="margin:0in;font-family:Arial;font-size:11.0pt"
lang="en-US"><span style="font-weight:bold">We are looking forward
to your application!</span></p>
<p
style="margin-top:0pt;margin-bottom:6pt;line-height:13pt;font-family:Arial;font-size:11.0pt;color:#00000A"> </p>
<p style="margin:0in;font-size:11.0pt"><span
style="font-weight:bold;
font-family:Arial">See here for more information:<span
style="mso-spacerun:yes"> </span></span><a
href="https://short.sg/j/17430658"><span
style="font-family:Calibri">https://short.sg/j/17430658</span></a></p>
<pre class="moz-signature" cols="72">--
Administration
Weierstrass Institute for Applied Analysis and Stochastics
Mohrenstrasse 39
10117 Berlin, Germany
Phone: +49 (0)30 20372 557
Fax: +49 (0)30 20372 329
URL: <a class="moz-txt-link-freetext" href="http://www.wias-berlin.de/~sill/?lang=1">http://www.wias-berlin.de/~sill/?lang=1</a>
</pre>
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