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<p class="MsoNormal"><span lang="EN-GB">A postdoctoral researcher
position is open in Prof. Dr. David Ginsbourger's<span> </span></span><a href="https://www.imsv.unibe.ch/research/research_groups/uncertainty_quantification_and_spatial_statistics/index_eng.html" title="Unmangled Microsoft Safelink"><span lang="EN-GB">uncertainty
quantification and spatial statistics group</span></a><span lang="EN-GB"><span> </span>at the Institute of Mathematical
Statistics and Actuarial Science, University of Bern,
Switzerland.<br>
<br>
<b><span>Context:</span></b><span> The recruited postdoctoral
researcher will be affiliated with Prof. Dr. David
Ginsbourger's group. The group is involved in collaborations
with national and international partners from various research
fields. These include geosciences, with a history of
collaborations around stochastic modelling in hydrogeology,
geophysical inversion, and atmospheric sciences (with links to
the Oeschger Center of Climate Change Research). Recently, the
group has also been increasingly collaborating with the
Institute of Social and Preventive Medicine and the University
Hospital around statistical machine learning for biomedical
applications.</span><br>
<br>
<b><span>Project:</span></b><span> The recruited postdoctoral
researcher will reinforce the current efforts of the group in
pushing further the limits of kernel methods to work with data
in and beyond Euclidean settings. Of special interest classes
are kernel embedding approaches, particularly well-suited to
work with set- and distribution-valued inputs, but there is
also room for further research directions depending on the
candidate's research interests and motivation. Several ongoing
or emerging collaborations (around paleo-climatology,
psychology, medicine, etc.) call for thorough approaches to
model similarity and estimate related parameters. This
position will give the opportunity to candidates with a strong
theoretical background to enrich their research profile with
application-driven research while cultivating their passion
for statistical methodology. </span><br>
<span> </span><br>
<b><span>Sought profile:</span></b><span> The ideal candidate
will have recently earned or be about to finish their PhD
degree in statistics or neighbouring subjects with a strong
mathematical component, a genuine interest in statistical data
science and applications thereof, a taste for both theoretical
investigations and numerical experiments, and solid
programming skills.</span></span><span lang="EN-GB"><span> </span></span><span lang="EN-GB">The salary will be at the<span> </span></span><a href="https://www.unibe.ch/unibe/portal/content/e152701/e322683/e325053/e916384/ul_rgl_postdoc_en_ger.pdf" title="Unmangled Microsoft Safelink"><span lang="EN-GB">level
foreseen by the University of Bern</span></a><span lang="EN-GB">. There might be a possibility to be involved in
teaching and/or consulting duties. The funding is secured for up
to 18 months with the starting date of May 1st 2023 or as can be
arranged by mutual agreement.</span><span lang="EN-GB"></span></p>
<p class="MsoNormal"><b><span lang="EN-GB">Applications should
contain</span></b><span lang="EN-GB">: (1) a letter in which
the applicants describe their research interests and motivations
as well as a 1-page research proposal, (2) a complete CV, (3)
copies of relevant diplomas, certificates and transcript of
records, (4) an electronic version of a research work (PhD
thesis or other scientific publication), (5) contact information
of 2 – 3 references.</span><span lang="EN-GB"></span></p>
<p class="MsoNormal"><span lang="EN-GB">Applications and inquiries
should be sent to Prof. Dr. David Ginsbourger (respectively via
this<span> </span><a href="https://ohws.prospective.ch/public/v1/jobs/6366027e-b758-42b7-9954-e734719decc0" title="Unmangled Microsoft Safelink">link</a><span> </span>and
via e-mail to<span> </span><a href="mailto:david.ginsbourger@unibe.ch" title="mailto:david.ginsbourger@unibe.ch" class="moz-txt-link-freetext">david.ginsbourger@unibe.ch</a>) </span></p>
<br>
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David Ginsbourger<br>
Bern<br>
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