ENGAGE: Interactive Machine Learning Accelerating Progress in Science, An Emerging Theme of ICT Research
Our
vision is to establish and lead a new theme in ICT research based on Interactive Machine Learning (IML). Our expansion of IML will give scientists and non-ICT specialists unprecedented access to cutting-edge Machine Learning algorithms by providing a human-computer
interface by which they can directly interact with large scale data and computing resources in an intuitive visual environment. In addition, the outcome of this particular project will have a direct transformative impact on the sciences by making it possible
for non-programming individuals (scientists), to create systems that semi-automatically detect objects and events in vast quantities of A) audio and B) visual data. By working together across two parallel, highly interconnected streams of ICT research, we
will develop the foundations of statistical methodology, algorithms and systems for IML. As an exemplar, this project partners with world leading scientists grappling with the challenge of analysing enormous quantities of heterogeneous data being generated
in Biodiversity Science.
Duties and Responsibilities
The Research Associate will work with Professor Mark Girolami on the EPSRC funded project ENGAGE: Interactive Machine Learning Accelerating Progress in Science, An Emerging Theme of ICT Research
http://gow.epsrc.ac.uk/NGBOViewGrant.aspx?GrantRef=EP/K015664/1. The aim of the project is to establish and lead a new theme in ICT research based on Interactive Machine Learning
(IML). This expansion of IML will give scientists and non-ICT specialists unprecedented access to cutting-edge Machine Learning algorithms by providing a human-computer interface by which they can directly interact with large scale data and computing resources
in an intuitive visual environment.
The post is available from March 2013 (or as soon as possible thereafter) and is initially funded for 36 months.
Key Requirements
Candidates should hold a PhD (or equivalent qualification) in Statistical Pattern Recognition, Machine Learning, or Information Retrieval. It is essential that the successful candidate will possess extensive and current
working experience of developing Computational Bayesian methods, in particular MCMC and Manifold MCMC schemes.
Further Details
A job description and person specification can be accessed at the following URL, together with some details about the department.
Informal enquiries regarding the vacancy may be addressed to Professor Mark Girolami, email:
[log in to unmask], tel: +44(0)20 7679 1861.
For any queries regarding the application process please contact Mrs Deepti Jayawardena Wilkinson, email:
[log in to unmask], tel: +44 (0)20 7679 1876.
Closing Date3 Mar 2013
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