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<p style="line-height: 100%; margin-bottom: 0in;">
<font face="Liberation Serif, serif">We are recruiting for the
Autumn
2025 cohort for the Centre for Doctoral Training in Mathematics
for
Our Future Climate! We have several PhD position opportunities
at the
intersection of data assimilation and machine learning, as well
as on
predictability, at the University of Reading.</font></p>
<p style="line-height: 100%; margin-bottom: 0in;"><font face="Liberation Serif, serif">The
positions are for fully funded 4-year PhD studentship.
Interested
students should apply as soon as possible through the University
of
Reading's application page at
<font color="#3465a4"><u><a class="moz-txt-link-freetext" href="https://www.reading.ac.uk/maths-and-stats/phd/mathematics-for-our-future-climate">https://www.reading.ac.uk/maths-and-stats/phd/mathematics-for-our-future-climate</a></u></font></font></p>
<p style="line-height: 100%; margin-bottom: 0in;"><font face="Liberation Serif, serif">Feel
free to contact me (<font color="#3465a4"><u><a class="moz-txt-link-abbreviated" href="mailto:e.bach@reading.ac.uk">e.bach@reading.ac.uk</a></u></font>)
with any questions and share this with anyone who may be
interested! </font>
</p>
<p style="line-height: 100%; margin-bottom: 0in;"><font face="Liberation Serif, serif"><b>Project
descriptions</b></font></p>
<p style="font-weight: normal; line-height: 100%; margin-bottom: 0in;">
<font face="Liberation Serif, serif"><i>Machine Learning
Approaches
in Bayesian and Ensemble Data Assimilation</i></font></p>
<p style="line-height: 100%; margin-bottom: 0in;"><font face="Liberation Serif, serif">Data
assimilation (DA), the process of combining model predictions
with
observations, is essential for weather forecasting.
Computational
limitations render typical DA algorithms suboptimal. This
project
will use machine learning to infer new DA algorithms that are as
close to optimality as possible, in order to improve forecasts
and
quantify uncertainty.</font></p>
<p style="line-height: 100%; margin-bottom: 0in;"><font face="Liberation Serif, serif"><i>Partners</i>:
Turing Institute</font></p>
<p style="line-height: 100%; margin-bottom: 0in;"><br>
</p>
<p style="line-height: 100%; margin-bottom: 0in;"><font face="Liberation Serif, serif"><i>Large
ensembles of machine learning forecasts for advanced nonlinear
filters in atmospheric data assimilation</i></font></p>
<p style="line-height: 100%; margin-bottom: 0in;"><font face="Liberation Serif, serif">Recently,
machine learning (ML) weather forecasting models have shown
deterministic forecast skill approaching that of physics-based
models, at a small fraction of the computational cost. This
provides
the opportunity to create very large ensembles of ML forecasts,
with
the potential to improve data assimilation (DA), the process of
optimally combining forecasts and observations to improve the
accuracy of weather predictions.</font></p>
<p style="line-height: 100%; margin-bottom: 0in;"><font face="Liberation Serif, serif"><i>Partners</i>:
European Centre for Medium-Range Weather Forecasting (ECMWF)</font></p>
<p style="line-height: 100%; margin-bottom: 0in;"><br>
</p>
<p style="line-height: 100%; margin-bottom: 0in;"><font face="Liberation Serif, serif"><i>The
signal-to-noise problem in weather and climate forecasts</i></font></p>
<p style="line-height: 100%; margin-bottom: 0in;"><font face="Liberation Serif, serif">A
puzzling phenomenon, the“Signal-to-Noise Paradox (SNP)”, has
been
observed in climate forecasts: reality appears to be more
predictable
than the forecasts are suggesting. This project will use machine
learning to analyse the SNP statistically. Furthermore,
potential
dynamical mechanisms for the SNP will be identified using
simplified
climate models.</font></p>
<p style="line-height: 100%; margin-bottom: 0in;"><font face="Liberation Serif, serif"><i>Partners</i>:
European Centre for Medium-Range Weather Forecasting (ECMWF)</font></p>
<p style="line-height: 100%; margin-bottom: 0in;"><br>
</p>
<p style="line-height: 100%; margin-bottom: 0in;"><font face="Liberation Serif, serif"><b>About
the MFC CDT</b></font></p>
<p><font face="Liberation Serif, serif">Are you passionate about
using mathematics to tackle the pressing challenges of climate
change? The EPSRC Centre for Doctoral Training in the
Mathematics for
our Future Climate (MFC CDT) invites you to apply for our
exciting
PhD programme. A dynamic and interdisciplinary PhD programme
that
harnesses the power of mathematics to address the urgent issues
presented by climate change. Jointly run by Imperial College
London,
the University of Reading, and the University of Southampton,
and a
range of partners across business, industry, charities, and
government.</font></p>
<p><font face="Liberation Serif, serif">The MFC CDT will train
highly
skilled mathematicians to become future leaders in innovative
research, developing environmental prediction technologies,
interpreting very large datasets relating to the Earth system,
and
modelling the risk associated with extreme weather and climate
change.</font></p>
<p><font face="Liberation Serif, serif"><b>Why Choose the MFC CDT
PhD
Programme?</b></font></p>
<ul>
<li>
<p><font face="Liberation Serif, serif"><b>Innovative Research
Opportunities:</b></font><font face="Liberation Serif, serif"> Engage in research focused
on weather and climate modelling, data analysis, and novel
mathematical approaches to environmental challenges.</font></p>
</li>
<li>
<p><font face="Liberation Serif, serif"><b>Interdisciplinary
Collaboration:</b> Work with experts from diverse fields,
including climate science, atmospheric physics, and related
disciplines.</font></p>
</li>
<li>
<p><font face="Liberation Serif, serif"><b>Cohort Culture:</b> Be
part of a vibrant cohort-based research environment and
enhance your personal skills through a bespoke training
programme.</font></p>
</li>
<li>
<p><font face="Liberation Serif, serif"><b>Tailored Internships:</b> Gain
practical experience with external partners in key sectors
such as insurance, energy, water, and marine industries.</font></p>
</li>
<li>
<p><font face="Liberation Serif, serif"><b>State-of-the-Art
Facilities:</b> Access cutting-edge facilities and
resources to support your research endeavours.</font></p>
</li>
<li>
<p><font face="Liberation Serif, serif"><b>Mentorship from
Renowned Faculty:</b> Benefit from guidance by experienced
faculty members dedicated to your academic and professional
growth.</font></p>
</li>
<li>
<p><font face="Liberation Serif, serif"><b>Fully Funded
Studentships:</b> Receive a stipend, including a London
weighting, PhD fees for 4 years, and a generous allowance
for research-related activities.</font></p>
</li>
</ul>
<p><font face="Liberation Serif, serif"><b>Join Us in Shaping the
Future</b></font></p>
<p><font face="Liberation Serif, serif">Your expertise and passion
for mathematics can play a pivotal role in advancing our
understanding of climate change. Applications are now open to
become
part of a community dedicated to making a positive impact on the
world. For more information and to apply, visit </font><a href="https://mfccdt.ac.uk/"><font color="#3465a4"><font face="Liberation Serif, serif"><u>https://mfccdt.ac.uk/</u></font></font></a><font face="Liberation Serif, serif">
or contact the Admissions team on </font><a href="mailto:Admission.CDT-MFC@reading.ac.uk"><font color="#3465a4"><font face="Liberation Serif, serif"><u>Admission.CDT-MFC@reading.ac.uk</u></font></font></a><font face="Liberation Serif, serif">
</font>
</p>
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