Looking for new opportunities for the new year? Have a few loose ends
over the holiday period? Why not apply for two post-doc opportunities in
the University of Oxford?
Festive wishes
Mike
__________________________________
Michael A Osborne
Dyson Associate Professor in Machine Learning, Engineering Science
Co-Director, Oxford Martin Programme on Technology and Employment
Faculty Member, Oxford-Man Institute of Quantitative Finance
Official Fellow, Exeter College
University of Oxford
+44 (0)1865 273007
http://www.robots.ox.ac.uk/~mosb
http://twitter.com/maosbot
## Postdoctoral Research Assistant in Machine Learning
### Department of Engineering Science, Oxford
*Grade 7: £30,738 - £37,768 p.a.*
More details and applications here: https://is.gd/6PKtaR. The closing
date for applications is **12.00 midday on 5th January 2017.**
We are seeking a full-time Postdoctoral Research Assistant to join the
machine learning research group at the Department of Engineering Science
(central Oxford). The post is fixed-term to 31 May 2018. The post will
involve work on two projects (sequentially): the first funded by Pearson
and Nesta (until 28 February 2017) and the second by the Health
Foundation (thereafter).
Your role in both projects is to develop novel probabilistic machine
learning algorithms for economic data characterising the future of
employment. The first project aims to shed light on the mix of skills
and competencies that will be required for the types of jobs that the US
and UK economies will need in 15 years’ time, and has been described
in blog posts from
[Pearson](http://blog.pearson.com/learning-needs-a-plan-for-the-revolution-we-can-already-glimpse/)
and
[Nesta](http://www.nesta.org.uk/blog/employment-2030-skills-competencies-and-implications-learning),
and in an article from
[Quartz](http://qz.com/749629/what-skills-will-human-workers-need-when-robots-take-over-new-research-will-let-the-machines-decide/).
The second project examines automation and computerisation in UK primary
healthcare delivery; the project’s website is
[here](http://healthautomation.oii.ox.ac.uk).
You should possess a good first degree in Engineering, Computer Science,
Mathematics, Statistics, Economics or similar, with specialisation in
probabilistic models and have or are about to complete a PhD in a
relevant area. You will be required to upload a covering
letter/supporting statement, including a brief statement of research
interests (describing how past experience and future plans fit with the
advertised position), CV and the details of two referees as part of your
online application. Informal enquiries may be addressed to Prof Michael
Osborne (email: m...@robots.ox.ac.uk).
The department holds an Athena Swan Bronze award, highlighting its
commitment to promoting women in Science, Engineering and Technology.
## Postdoctoral Research Assistant in Machine Learning by Bayesian
Optimisation for Experimental Research in Quantum Nanodevices
### Department of Materials, Parks Road, Oxford
*Grade 7: Salary in the range £30,738 - £34,576 p.a.*
More details and applications here: https://is.gd/mXuMDF.
The closing date for applications is **12.00 midday on 5th January
2017.**
We are seeking to appoint a Postdoctoral Research Assistant whose aim
will be to harness Machine Learning techniques for the process of
scientific discovery. Duties will include development and application of
Bayesian Optimisation for measurements of single-molecule devices, and
training them on simulated experimental data. The post is available for
up to 3 years and is under the supervision of Professor Andrew Briggs.
The project's overarching aim is to identify properties of molecular
systems that are desirable in future information processing, especially
lower power switching to minimise energy costs (and consequent
environmental impact). You will engage and work collaboratively with
others involved in the programme including Professor Michael Osborne,
Department of Engineering Science, who will supervise the development of
the machine learning methods.
You will have a good first degree and a completed doctorate (or nearly
completed) in a relevant discipline. You will have expertise and
experience in software engineering, along with demonstrated expertise in
model-based machine learning.
The Department of Materials is actively promoting the provision of a
family friendly working environment and together with the University of
Oxford recognises the demands of work/life balance. Therefore for this
project we encourage applications from candidates who wish either to
hold these positions on a full-time, or part-time basis or need
flexibility in their working hours and will discuss these opportunities
with shortlisted applicants at interview.
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