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<p><span>We invite applications for four Postdoctoral Research
Associates (PDRA) to join the EPSRC Hub on the Mathematical
and Computational Foundations of Artificial Intelligence.
One PDRA will be recruited for each of the following four
research themes: Learning with Structured & Geometric
Models, Low Effective-dimensional Learning Models, Implicit
Regularization, and Reinforcement Learning through
Stochastic Control. </span><span>A brief description of
each these is as follows (additional details are in the
further particulars): </span></p>
<p><span><strong>Learning with Structured and Geometric Models.</strong>
We will apply tools from manifold learning and Riemannian
optimisation to leverage the underlying manifold structure
for better training and novel network designs. </span></p>
<p><span><strong>Low Effective-dimensional Learning Models.</strong>
We will extend foundational theory of how large ML systems
can be regularised to have dramatically fewer trainable
parameters without sacrificing accuracy by analysing the use
of low-dimensional building blocks </span></p>
<p><span><strong>Implicit Regularization.</strong> We aim to
develop mathematical understanding of implicit
regularisation properties in deep neural networks to guide
the development of algorithmic paradigms aimed at combining
statistical optimality with computational efficiency. </span></p>
<p><span><strong>Reinforcement Learning through Stochastic
Control.</strong> We will develop methods from stochastic
control, which will provide a mathematically grounded
approach that has a well-posed continuous-time limit (as
opposed to traditional RL methods that are inherently
discrete and do not scale favourably for high frequency
observations without judicious hyper-parameter tuning). </span></p>
<p><span>The PDRAs will work with faculty across the
multi-university Hub, but will be employed by and directly
supervised by faculty within the Mathematical Institute at
the University of Oxford. Faculty within the Mathematical
Institute associated with the above work packages include
Profs. Cartis, Cohen, Hauser, Lambiotte, Reisinger,
Sirignano, and Tanner. </span></p>
<p><span>These are two-year, fixed-term position, funded by a
research grant from the EPSRC. The starting date of this
position is flexible with an earliest start date of 01 March
2025. </span></p>
<p><span>The successful candidates will be expected to conduct
research which falls within the remit of this large-scale
project and will have the opportunity to do so
collaboratively with other members of the hub, both at
Oxford and/or with hub partners which include universities
as well as companies and governmental organisations. </span><span>They
will contribute to the activities of the wider machine
learning and data science research group and write up the
results of their work, with co-authors, for publication in
refereed journals and proceedings. There will be
opportunities to contribute a small amount of teaching to
the department, of at most three hours a week during the
academic terms.</span></p>
<p><span>You will have, or be close to completing, a PhD in
mathematics or a related discipline, and possess sufficient
specialist knowledge in the discipline to work within
established research programmes. Excellent communication
skills are essential, including the ability to write for
publication, present research proposals and results, and
represent the research group at meetings.</span></p>
<p> </p>
<p><span>We proudly hold a departmental Athena SWAN Silver Award
and an institutional Race Equality Charter Bronze Award,
which guide our progress towards advancing racial and gender
equality. As part of our strategic aim to improve staff
equality and diversity, we would particularly welcome
applications from women and BME candidates, who are
currently under-represented in positions of this type within
the department.</span></p>
<p> </p>
<p><span>Please direct informal enquiries to the Recruitment
Coordinator (email: </span><a href="mailto:recruitment@maths.ox.ac.uk" title="mailto:recruitment@maths.ox.ac.uk" class="moz-txt-link-freetext">recruitment@maths.ox.ac.uk</a><span>),
quoting vacancy reference <strong>176180</strong>.</span></p>
<p><span>Applicants will be selected for interview purely based
on their ability to satisfy the selection criteria as
outlined in full in the job description. You will be
required to upload a statement setting out how you meet the
selection criteria, a curriculum vitae including full list
of publications, a statement of research interests, and the
contact details of two referees as part of your online
application. <strong>(NOTE: Applicants are responsible for
contacting their referees and making sure that their
letters are received by the closing date).</strong></span></p>
<p><span>Applications for this vacancy are to be made online. To
apply for this vacancy and for further information,
including a job description and selection criteria, please
click on the link below:</span></p>
<p><span lang="EN-US"></span></p>
<p><a href="https://my.corehr.com/pls/uoxrecruit/erq_jobspec_details_form.jobspec?p_id=176180" title="Unmangled Microsoft Safelink"><span lang="EN-US">https://my.corehr.com/pls/uoxrecruit/erq_jobspec_details_form.jobspec?p_id=176180</span></a><span lang="EN-US"></span></p>
<p><span lang="EN-US">Applications received before <strong>12.00
noon</strong> UK time on <strong>Monday, 02 December
2024 </strong>will receive full consideration.
Applications after this date will be considered at the
discretion of the committee.</span></p>
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<div class="field field--name-field-vacancy-file field--type-file field--label-above">
<div class="field--label">Job description</div>
<div class="field--item"><span class="file file--mime-application-pdf file--application-pdf icon-before"><span class="file-icon"><span class="icon glyphicon glyphicon-file text-primary" aria-hidden="true"></span></span><span class="file-link"><a href="https://www.maths.ox.ac.uk/system/files/vacancies/176180_PDRA%20JD%20and%20Selection%20Criteria_0.pdf" title="Unmangled Microsoft Safelink">176180_PDRA JD and
Selection Criteria_0.pdf</a></span><span class="file-size">562.59
KB</span></span></div>
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<p></p>
<p>With best wishes Coralia Cartis</p>
<p>Professor of Numerical Optimization,</p>
<p>Mathematical Institute, University of Oxford </p>
<br>
<br>
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Coralia Cartis<br>
Oxford<br>
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