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    <p
style="margin-left:.375in;margin-top:0pt;margin-bottom:6pt;font-family:Arial;font-size:11.0pt;color:#00000A">WIAS
      invites applications for a </p>
    <p
style="margin-left:.375in;margin-top:6pt;margin-bottom:6pt;font-family:Arial;font-size:20.0pt;color:#00000A"><span
        style="font-weight:bold">Postdoc position
        (f/m/d)</span></p>
    <p
      style="margin-left:.375in;margin-top:0pt;margin-bottom:12pt;font-family:
      Arial;font-size:11.0pt;color:black"><span style="font-weight:bold">(Ref.
        22/32)</span></p>
    <p
style="margin-left:.375in;margin-top:0pt;margin-bottom:4pt;font-family:Arial;font-size:11.0pt;color:#00000A"><span
        style="font-weight:bold">at the
        intersection of the areas machine learning / mathematical
        optimization /
        optimal control with a focus on robustness under distribution
        shift</span> in
      Berlin, Germany. </p>
    <p
style="margin-left:.375in;margin-top:6pt;margin-bottom:6pt;font-family:Arial;font-size:11.0pt;color:black">The
      position is associated with the third-party
      funded research project lead by Dr. Jia-Jie Zhu (WIAS Berlin) and
      Prof. Michael
      Hintermüller (WIAS/ Humboldt-Universität zu Berlin)</p>
    <p
style="margin-left:.375in;margin-top:6pt;margin-bottom:6pt;font-family:Arial;font-size:11.0pt;color:black"><span
        style="font-weight:bold">Data-driven Robust
        Model Predictive Control under Distribution Shift</span></p>
    <p
style="margin-left:.375in;margin-top:6pt;margin-bottom:6pt;font-family:Arial;font-size:11.0pt"><span
        style="color:black">within the Berlin Mathematics
        Research Center MATH+</span><span style="color:#00000A"> lead by
        Dr. Jia-Jie
        Zhu (WIAS Berlin) and Prof. Michael Hintermüller (WIAS/
        Humboldt-Universität
        zu Berlin). The initial funding will run for two years, starting
        January 2022.
        The applicants should have completed their Ph.D. degrees by the
        starting date
        of the project.</span></p>
    <p
style="margin-left:.375in;margin-top:0pt;margin-bottom:6pt;font-family:Arial;font-size:11.0pt;color:#00000A">Motivated
      by numerous partial differential
      equations related practical applications, a pressing challenge for
      data-driven
      optimization and control systems is the ubiquitous distribution
      shift, which
      implies higher demand for the robustness of the machine learning
      system design.
      The project, funded by the Excellence Cluster MATH+: The Berlin
      Mathematics
      Research Center, aims to address the issue of data-driven robust
      control and
      optimization of dynamical systems under data distribution shifts,
      using principled
      tools from applied mathematics and statistical machine learning as
      well as
      reinforcement learning. We invite postdoctoral candidates whose
      scholar
      profiles are mainly theoretical and exhibit proven excellence in
      research. We
      specifically prefer two types of mathematical research
      experiences:</p>
    <ol
      style="direction:ltr;unicode-bidi:embed;margin-top:0in;margin-bottom:
0in;font-family:Calibri;font-size:11.0pt;font-weight:normal;font-style:normal"
      type="1">
      <li style="margin-top:0;margin-bottom:0;vertical-align:middle;
        margin-top:0pt;margin-bottom:6pt" value="1"><span
          style="font-family:Arial;
          font-size:11.0pt;font-weight:normal;font-style:normal;font-family:Arial;
          font-size:11.0pt;color:#00000A">either in principled
          statistical machine learning/reinforcement learning theory
          (related to dynamical systems, time series, Markov decision
          process (MDP), control theory). Those qualifications are
          demonstrated, among others, by high-quality publications in
          credible venues such as
          NeurIPS/ICLR/AISTATS/ICML/CoLT/JMLR/L4DC/IEEE-CDC.</span></li>
      <li
style="margin-top:0;margin-bottom:0;vertical-align:middle;margin-top:0pt;
        margin-bottom:6pt"><span
          style="font-family:Arial;font-size:11.0pt; color:#00000A">and/or
          in applied mathematics, in optimization, numerical analysis,
          optimal control, dynamical systems (PDEs and S(P)DEs),
          data-driven modeling of dynamics, model predictive control
          (MPC). Those qualifications are demonstrated, among others by
          relevant publications in credible venues such as SIAM
          OPT/CON/COAP/COCV/MathProg.</span></li>
    </ol>
    <p
style="margin-left:.375in;margin-top:0pt;margin-bottom:4pt;font-family:Arial;font-size:11.0pt;color:#00000A"><span
        style="font-weight:bold">What we offer:</span></p>
    <ul style="direction:ltr;unicode-bidi:embed;margin-top:0in;
      margin-bottom:0in" type="disc">
      <li style="margin-top:0;margin-bottom:0;vertical-align:middle"><span
          style="font-family:Arial;font-size:11.0pt;color:#00000A">Close
          mentorship: the postdoc candidate will receive responsible and
          careful mentorship. We emphasize fostering a healthy
          mentor-mentee relationship.</span></li>
      <li style="margin-top:0;margin-bottom:0;vertical-align:middle"><span
          style="font-family:Arial;font-size:11.0pt;color:#00000A">WIAS
          Berlin is a premier research institution known for its
          strength in optimization, optimal control, dynamical systems,
          and applied mathematics in general. It has hosted flagship
          conferences in mathematical optimization such as ICCOPT 2019.</span></li>
      <li style="margin-top:0;margin-bottom:0;vertical-align:middle"><span
          style="font-family:Arial;font-size:11.0pt;color:#00000A">A
          certified (Audit berufundfamilie) family-friendly work
          environment.</span></li>
      <li
style="margin-top:0;margin-bottom:0;vertical-align:middle;margin-top:0pt;
        margin-bottom:6pt"><span
          style="font-family:Arial;font-size:11.0pt; color:#00000A">Berlin
          is one of the most culture-rich and diverse international
          cities in the world. It offers endless opportunities to enjoy
          life outside work, while being very affordable compared to
          other major cities. Neither the job nor living in Berlin
          requires German language (although WIAS offers free German
          courses). We highly welcome international applications.
          Scientifically, Berlin offers a rich landscape with numerous
          opportunities for research, as well as job prospects in
          academia and industry.</span></li>
    </ul>
    <p
style="margin-left:.375in;margin-top:0pt;margin-bottom:6pt;font-family:Arial;font-size:11.0pt"><span
        style="color:#00000A">Please direct scientific queries
        to Dr. J.-J. Zhu (</span><a href="mailto:Zhu@wias-berlin.de"
        class="moz-txt-link-freetext">Zhu@wias-berlin.de</a><span
        style="color:#00000A">).</span></p>
    <p
style="margin-left:.375in;margin-top:0pt;margin-bottom:4pt;font-family:Arial;font-size:11.0pt;color:#00000A">The
      envisioned starting date is as soon as
      possible and the appointment is limited until December 31, 2023.
      The work
      schedule is 39 hours per week, and the salary is according to the
      German TVoeD
      Bund scale.</p>
    <p
style="margin-left:.375in;margin-top:0pt;margin-bottom:4pt;font-family:Arial;font-size:11.0pt;color:#00000A"> </p>
    <p
style="margin-left:.375in;margin-top:0pt;margin-bottom:4pt;font-family:Arial;font-size:11.0pt;color:#00000A">The
      Weierstraß Institute is an equal
      opportunity employer. We explicitly encourage female researchers
      to apply for
      the offered position. Among equally qualified applicants, disabled
      candidates
      will be given preference.</p>
    <p
style="margin-left:.375in;margin-top:0pt;margin-bottom:4pt;font-family:Arial;font-size:11.0pt;color:#00000A"><span
        style="font-weight:bold"> </span></p>
    <p
style="margin-left:.375in;margin-top:0pt;margin-bottom:4pt;font-family:Arial;font-size:11.0pt;color:#00000A"><span
        style="font-weight:bold">How to apply:</span></p>
    <p
style="margin-left:.375in;margin-top:6pt;margin-bottom:6pt;font-family:Arial;font-size:11.0pt"><span
        style="color:#00000A">Please upload complete
        application documents including a cover letter, curriculum
        vitae(CV), relevant
        certificates, and the Ph.D. thesis (as draft if not finalized)
        as soon as
        possible via our online </span><a
        href="https://short.sg/j/22924556">job-application
        facility</a><span style="color:#00000A"> using the button “</span><a
        href="https://short.sg/a/22924556">Apply online</a><span
        style="color:#00000A">”.
      </span></p>
    <p
style="margin-left:.375in;margin-top:6pt;margin-bottom:6pt;font-family:Arial;font-size:11.0pt;color:#00000A"><span
        style="font-weight:bold">The
        advertisement is open with immediate effect and will remain open
        until the
        position will be filled.</span></p>
    <p
style="margin-left:.375in;margin-top:0pt;margin-bottom:4pt;font-family:Arial;font-size:11.0pt;color:#00000A"><span
        style="font-weight:bold"> </span></p>
    <p
style="margin-left:.375in;margin-top:0pt;margin-bottom:4pt;font-family:Arial;font-size:11.0pt;color:#00000A"><span
        style="font-weight:bold">We are looking
        forward to your application!</span></p>
    <p
style="margin-left:.375in;margin-top:0pt;margin-bottom:6pt;font-family:Arial;font-size:11.0pt;color:black"> </p>
    <p
      style="margin:0in;margin-left:.375in;font-family:Arial;font-size:11.0pt"><span
        style="font-weight:bold">See here for more information: </span><a
        href="https://short.sg/j/22924556" class="moz-txt-link-freetext">https://short.sg/j/22924556</a><span
        style="font-weight:bold"><span style="mso-spacerun:yes">  </span></span></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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