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Senior Research Associate in Embedded Machine Learning for Space Applications

Applications for this vacancy closed on 4 December 2019 at 12:00PM
We are pleased to announce the availability of a full-time Senior Research
Associate position in Embedded Machine Learning with special focus on
applications in space (including earth orbit or deep space), available for up
to 3 years. Researchers with a strong track record of embedded machine
learning with space-related applications will be considered, but with a
preference towards researchers who have made contributions in system resource
efficiency with this domain – as well as to the development of open source
software in the area.



Successful applicants will contribute to improving the state of the art in
devising embedded machine learning techniques, especially within the context
of limited system resources (e.g. memory, compute, battery) and hardware
designed for space conditions (e.g. radiation hardened processors). Such
technology is a critical enabler for usage of machine learning within systems
deployed in space including those placed in earth orbit or sent on deep space
missions. Traditionally the limited system resources of space deployed systems
have made it prohibitive for machine learning to be locally executed. However,
if through methods that allow machine learning to be more efficient, machine
learning can be directly integrated within space systems then the need for
remote co-ordination from terrestrial-based systems over data communication
networks can be reduced. Positive side-effects include longer mission times as
energy hungry communications can be performed less. Successful applicants will
join the OxMLSys lab (http://mlsys.cs.ox.ac.uk) under the supervision of
Professor Nicholas Lane (http://niclane.org) and conduct world leading
research in this area, and engage strongly with the academic community as well
as with industrial partners.



Whilst the role is a Grade 8 position, we would be willing to consider
candidates with potential but less experience who are seeking a development
opportunity, for which an initial appointment would be at Grade 7 (£32,236 -
£39,609 p.a.) with the responsibilities adjusted accordingly (for Grade 7, you
would be expected to hold a doctoral degree in computer science or be close to
completion). This would be discussed with applicants at interview/appointment
where appropriate.



The closing date for applications is 12.00 noon on Wednesday 4 December 2019.



Our staff and students come from all over the world and we proudly promote a
friendly and inclusive culture. Diversity is positively encouraged, through
diversity groups and champions, for example www.cs.ox.ac.uk/aboutus/women-cs-
oxford/index.html, as well as a number of family-friendly policies, such as
the right to apply for flexible working and support for staff returning from
periods of extended absence, for example maternity leave.

dc:spatial
Department of Computer Science, Parks Road, Oxford.
Subject
oo:contact
oo:formalOrganization
oo:organizationPart
vacancy:applicationClosingDate
2019-12-04 12:00:00+00:00
vacancy:applicationOpeningDate
2019-11-27 09:00:00+00:00
vacancy:furtherParticulars
vacancy:internalApplicationsOnly
False
vacancy:salary
type
comment
We are pleased to announce the availability of a full-time Senior Research
Associate position in Embedded Machine Learning with special focus on
applications in space (including earth orbit or deep space), available for up
to 3 years. Researchers with a strong track record of embedded machine
learning with space-related applications will be considered, but with a
preference towards researchers who have made contributions in system resource
efficiency with this domain – as well as to the development of open source
software in the area.



Successful applicants will contribute to improving the state of the art in
devising embedded machine learning ...

We are pleased to announce the availability of a full-time Senior Research Associate position in Embedded Machine Learning with special focus on applications in space (including earth orbit or deep space), available for up to 3 years. Researchers with a strong track record of embedded machine learning with space-related applications will be considered, but with a preference towards researchers who have made contributions in system resource efficiency with this domain – as well as to the development of open source software in the area.


Successful applicants will contribute to improving the state of the art in devising embedded machine learning ...

label
Senior Research Associate in Embedded Machine Learning for Space Applications
notation
144169
based near
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