Postdoctoral Research Associate in the Mathematical and Computational Foundations of Artificial Intelligence
Applications for this vacancy closed on 2 December 2024 at 12:00PM
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.
A brief description of each these is as follows (additional details are in the
further particulars):
**Learning with Structured and Geometric Models.** We will apply tools from
manifold learning and Riemannian optimisation to leverage the underlying
manifold structure for better training and novel network designs.
**Low Effective-dimensional Learning Models.** 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
**Implicit Regularization.** 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.
**Reinforcement Learning through Stochastic Control.** 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).
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.
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.
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.
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.
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.
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.
Please direct informal enquiries to the Recruitment Coordinator (email:
recruitment@maths.ox.ac.uk), quoting vacancy reference **176180**.
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. **(NOTE: Applicants are responsible for contacting their
referees and making sure that their letters are received by the closing
date).**
Applications received before **12.00 noon** UK time on **Monday, 02 December
2024** will receive full consideration. Applications after this date will be
considered at the discretion of the committee.
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.
A brief description of each these is as follows (additional details are in the
further particulars):
**Learning with Structured and Geometric Models.** We will apply tools from
manifold learning and Riemannian optimisation to leverage the underlying
manifold structure for better training and novel network designs.
**Low Effective-dimensional Learning Models.** 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
**Implicit Regularization.** 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.
**Reinforcement Learning through Stochastic Control.** 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).
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.
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.
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.
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.
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.
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.
Please direct informal enquiries to the Recruitment Coordinator (email:
recruitment@maths.ox.ac.uk), quoting vacancy reference **176180**.
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. **(NOTE: Applicants are responsible for contacting their
referees and making sure that their letters are received by the closing
date).**
Applications received before **12.00 noon** UK time on **Monday, 02 December
2024** will receive full consideration. Applications after this date will be
considered at the discretion of the committee.
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Andrew Wiles Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG
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| vacancy:applicationClosingDate |
2024-12-02 12:00:00+00:00
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| vacancy:applicationOpeningDate |
2024-10-24 09:00:00+01:00
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| vacancy:internalApplicationsOnly |
False
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| comment |
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. A brief description of each these is as follows (additional details are in the further particulars): Learning with Structured and Geometric Models. We will apply tools from manifold learning and Riemannian optimisation to leverage the underlying manifold structure for better training and novel network ... 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. A brief description of each these is as follows (additional details are in the further particulars): **Learning with Structured and Geometric Models.** We will apply tools from manifold learning and Riemannian optimisation to leverage the underlying manifold structure for better training and novel network designs. **Low ... |
| label |
Postdoctoral Research Associate in the Mathematical and Computational Foundations of Artificial Intelligence
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| notation |
176180
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| page |