Download Job description and selection criteria

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ENGINEERING SCIENCE
Job description and selection criteria
Job title
Postdoctoral Research Assistant in Probabilistic Programming
Division
Mathematical, Physical and Life Sciences Division
Department
Engineering Science
Location
Central Oxford
Grade and salary
Grade 7 £29,541 - £36,298 p.a.
Hours
Full time
Contract type
Fixed-term; two years.
Reporting to
Dr. Frank Wood, University Lecturer
Vacancy reference
111173
Additional
information
Two positions
Introduction
The University
The University of Oxford is a complex and stimulating organisation, which enjoys an
international reputation as a world-class centre of excellence in research and teaching. It
employs over 10,000 staff and has a student population of over 22,000.
Most staff are directly appointed and managed by one of the University’s 130 departments or
other units within a highly devolved operational structure - this includes over 6,500
‘academic-related’ staff (postgraduate research, computing, senior library, and administrative
staff) and over 2,700 ‘support’ staff (including clerical, library, technical, and manual staff).
There are also over 1,600 academic staff (professors, readers, lecturers), whose
appointments are in the main overseen by a combination of broader divisional and local
faculty board/departmental structures. Academics are generally all also employed by one of
the 38 constituent colleges of the University as well as by the central University itself.
Our annual income in 2011/12 was £1,016.1m. Oxford is one of Europe's most innovative
and entrepreneurial universities: income from external research contracts exceeds £409m
p.a., and more than 80 spin-off companies have been created.
For more information please visit www.ox.ac.uk/staff/about_the_university.html
The Mathematical, Physical, and Life Sciences Division
The Mathematical, Physical, and Life Sciences Division (MPLS) is one of the four academic
divisions of the University.
Oxford is widely recognised as one of the world's leading science universities. In the 2008
UK Research Assessment Exercise over 70% of research activity in MPLS was judged to be
world-leading (4*) or internationally excellent (3*), and Oxford was ranked first in the UK
across the mathematical sciences as a whole.
The MPLS division's ten departments and three interdisciplinary units span the full spectrum
of the mathematical, computational, physical, engineering and life sciences, and undertake
both fundamental research and cutting-edge applied work. We have over 6,000 students and
research staff, and generate over half of our funding from external research grants. Our
research addresses major societal and technological challenges and is increasingly
interdisciplinary in nature. We collaborate closely with colleagues in Oxford across the
medical sciences, social sciences and humanities, as well as with researchers from around
the world.
For more information, please visit:
http://www.mpls.ox.ac.uk/
Engineering Science Department
Engineering teaching and research takes place at Oxford in a unified Department of
Engineering Science whose academic staff are committed to a common engineering foundation
as well as to advanced work in their own specialities, which include most branches of the
subject. We have especially strong links with computing, materials science and medicine. The
Department employs about 90 academic staff (this number includes 13 statutory Professors
appointed in the main branches of the discipline, and 25 other professors in the Department); in
addition there are 9 Visiting Professors. There is an experienced team of teaching support staff,
clerical staff and technicians. The Department has well-equipped laboratories and workshops,
which together with offices, lecture theatres, library and other facilities have a net floor area of
about 22,000 square metres.
Teaching
We aim to admit 160-170 undergraduates per year, all of whom take a 4-year Engineering
Science course leading to the MEng degree. The course is accredited at MEng level by the
major engineering institutions. The syllabus has a common core extending through the first two
years. Specialist options are introduced in the third year, and the fourth year includes further
specialist material and a major project.
Research
Research in the Department is particularly strong. We have approximately 300 research
students and about 90 Research Fellows and Postdoctoral researchers. Direct funding of
research grants and contracts, from a variety of sources, amounts to an annual turnover of
approximately £13M in addition to general turnover of about £16M. In the Research
Assessment Exercise of 2008, 85% of the Department’s research was rated as 4* or 3*
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(world-leading or internationally excellent). The research activities of the department fall into
seven broad headings, though there is much overlapping in practice: Thermofluids; Materials
and Mechanics; Civil and Offshore; Information, Control and Vision; Electrical and
Optoelectronic; Chemical and Process; Biomedical Engineering.
For more information please visit:
http://www.eng.ox.ac.uk/
The University of Oxford is a member of the Athena SWAN Charter and holds an institutional
Bronze Athena SWAN award. The Department of Engineering Science holds a Departmental
Bronze Athena award in recognition of its efforts to introduce organisational and cultural
practices that promote gender equality in SET and create a better working environment for
both men and women.
Machine Learning At Oxford
The University of Oxford has a world-class core-strength in machine learning spanning three
departments; Information Engineering (Wood, Stephens, Osborne, Newman, Posner
Zisserman, Vidaldi, Torr, …), Statistics (Teh, Doucet, Caron, Holmes, …), and Computer
Science (de Freitas, Blumson, …). Exact numbers change frequently but combined counts
of ML postdocs run well into the tens and ML students run well into the hundreds. Cross
department collaboration is easy and encouraged with joint paper and grant writing, reading
groups, talk series, teas, and more happening all the time. Information Engineering, where
the successful candidates will work, resides in a state-of-the art modern building with
pleasant open-plan lab spaces, ample computing resources, and proximity to (but not
reliance on) the history and beauty for which Oxford is known.
Job description
Research topic
Machine Learning
Principal Investigator
/ supervisor
Dr. Frank Wood
Project team
Dr.’s Wood and de Freitas (Oxford), Mansingkha and Tenenbaum
(MIT), and Ghahramani (Cambridge)
Project web site
www.robots.ox.ac.uk/~fwood
Funding partner
The funds supporting this research project are provided by
DARPA under its Probabilistic Programming for Advanced
Machine Learning program (http://tinyurl.com/fwoxpp)
Recent publications
NA
Technical skills
Ideal candidates possess a) a strong machine learning
background (with strengths in unsupervised generative modelling,
general-purpose sampling-based inference, and Bayesian
nonparametrics), b) a strong computer science background (with
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strengths in automata theory, compiler and interpreter design,
and practical functional coding ability) and c) a strong statistics
and probability theory background. Candidates with strong
theoretical backgrounds in any or all of the three are strongly
encouraged to apply
Overview of the role
Oxford is part of an MIT/Oxford/Cambridge collaborative team. Dr. Wood (in Engineering)
and Dr. Nando de Freitas (in Computer Science) are tasked with engaging in research on
general-purpose inference and probabilistic programming applications. Oxford participants
are already closely collaborating with both MIT (Dr. Vikash Mansingkha and Dr. Joshua
Tenenbaum) on the development of a practical, scalable probabilistic programming
environment and Cambridge (Dr. Zoubin Ghahramani) on models, inference, and
applications.
Candidates will be responsible for conducting wide-ranging, largely self-determined research
at the intersection of probability, statistics, inference, modelling, compiler and language
theory, and applications. Candidates will also be responsible for contributing to the
development of a practical, deployable, scalable probabilistic programming system.
Candidates will be expected to travel between collaborator sites and to collaborate and
disseminate widely.
The positions are distinguished by the flexibility of the posts, the breadth and character of
the overall team, and the unique opportunity to collaboratively develop a working
probabilistic programming system.
Responsibilities/duties
Specific duties:
 Perform wide-ranging, largely self-determined research at the intersection of
probability, statistics, inference, modelling, compiler and language theory, and
applications.
 Contribute to the development of a practical, deployable, scalable probabilistic
programming system.
 Travel between partner sites and to collaborate and disseminate widely
Additional duties:
 Mentor and collaborate with existing students and postdocs in the Wood group and in
the Oxford machine learning community.
 Develop ideas for generating research income, and present detailed research
proposals to senior researchers
 Collaborate in the preparation of scientific reports and journal articles and occasionally
present papers and posters
 Represent the research group at external meetings/seminars, either with other
members of the group or alone
 The PDRAs may have the opportunity to teach (this includes lecturing, demonstrating,
small-group teaching, tutoring of undergraduates and graduate students and
supervision of masters projects in collaboration with principal investigators)
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Selection criteria
Essential
 Hold a relevant phD/Dphil (computer science, statistics, etc.), together with relevant
experience, for example:
a) a strong machine learning background (with strengths in unsupervised generative
modelling,
general-purpose
sampling-based
inference,
and
Bayesian
nonparametrics),
b) a strong computer science background (with strengths in automata theory,
compiler and interpreter design, and practical functional coding ability) and
c) a strong statistics and probability theory background.
 Have strong (ideally functional) programming and software engineering skills
 Have a strong record of prior publications in top-flight machine learning conferences
(NIPS, ICML, AISTATS, UAI, AAAI, etc.) and journals
 Possess sufficient specialist knowledge in the discipline to work within established
research programmes
 Ability to manage own academic research and associated activities
 Previous experience of contributing to joint publications/presentations
 Ability to contribute ideas for new research projects and materials for research income
generation
 Excellent English communication skills, including the ability to write for publication,
present research proposals and results, and represent the research group at
meetings
Desirable
 Experience of independently managing a discrete area of a research project
 Experience of actively collaborating in the development of research articles for
publication
Working at the University of Oxford
For further information about working at Oxford, please see:
www.ox.ac.uk/about_the_university/jobs/research/
How to apply
If you consider that you meet the selection criteria, click on the Apply Now button on the
‘Job Details’ page and follow the on-screen instructions to register as a user. You will then
be required to complete a number of screens with your application details, relating to your
skills and experience. When prompted, please provide details of two referees and indicate
whether we can contact them at this stage. You will also be required to upload a CV and
supporting statement which explains how you meet the selection criteria for the post. The
supporting statement should explain your relevant experience which may have been gained
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in employment, education, or you may have taken time away from these activities in order to
raise a family, care for a dependant, or travel for example. Your application will be judged
solely on the basis of how you demonstrate that that you meet the selection criteria outlined
above and we are happy to consider evidence of transferable skills or experience which you
may have gained outside the context of paid employment or education.
Please save all uploaded documents to show your name and the document type.
All applications must be received by midday on the closing date stated in the online
advertisement.
Should you experience any difficulties using the online application system, please email
[email protected]
To return to the online application at any stage, please click on the following link
www.recruit.ox.ac.uk
Please note that you will be notified of the progress of your application by automatic e-mails
from our e-recruitment system. Please check your spam/junk mail regularly to ensure that
you receive all e-mails.
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