UK:Research Associate Bayesian Tracking & Reasoning Over Time at University of Cambridge

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Salary scales: £24,049 to £27,047 for Research Assistant, £27,854 to £36,298 for Research Associate

Reference: NA01469

Closing date: 01 August 2013

Description: A fixed-term position until 31 May 2014 in the first instance exists for a Research Assistant/Associate in the Department of Engineering, to work on an EPSRC funded project, Bayesian Tracking and Reasoning over Time.  The post holder will be located in Central Cambridge, Cambridgeshire, UK. The successful applicant will work in the Signal Processing and Communications Laboratory under Prof. Simon Godsill and Dr. Sumeetpal Singh, the grant-holders. There will be collaboration with Lancaster University.

The project aims to provide new advances in computational methods for reasoning about many objects that evolve in a scene over time. Information about such objects arrives from sensors such as radar, sonar, LIDAR and video.  The new and exciting part of this project is in automated understanding of the ‘social interactions’ that underlie a multi-object scene. The outcomes from this project could cause a paradigm shift in tracking methodology if successful, moving away from the traditional viewpoint of a scene in which objects move independently of one another, towards an integrated viewpoint where object interactions are automatically learned and used in improved decision-making processes. Applications include vehicle tracking, mapping, animal behaviour modelling, economic models and social network modelling.

These problems can all be posed using probability theory, and in particular using Bayesian theory. While straightforward to pose, there are substantial challenges for our problem area in terms of how to pose the underlying prior models (what is a good way to model the random behaviour of networked objects in a scene?), and how do we carry out the demanding computational calculations that are required for many-object scenes? These modelling and computational challenges form a major part of the project, and require substantial new theoretical and applied algorithm development over the course of the project. We will develop novel computational methods based principally around Monte Carlo computing, in which carefully designed randomised data are used to approximate the integrations and optimisations required in the Bayesian approach.

The work will require the successful candidates to design and develop methodologies to address the above research objectives. This will involve planning and managing their own research activity and liaising with colleagues in the signal processing and communications research group and our research partners at Lancaster University on this project, as well as QinetiQ who are supporting the project through provision of data and evaluation of methods.

This is a project with a heavy focus on statistical modelling and computation for interacting many object systems using inaccurate and incomplete measurements. Candidates should have, or be about to be awarded, a PhD in Statistical Signal Processing, Applied Probability, Statistics, Machine Learning or a related area. Candidates with expertise in the following areas will be at an advantage: underlying mathematics of stochastic process models (including continuous-time), Markov Chain Monte Carlo, Particle Filtering, Bayesian methods and dynamic network models.

Fixed-term: The funds for this post are available until 31 May 2014 in the first instance.

This appointment is subject to a health assessment. Whether an outcome is satisfactory will be determined by the University.

Further details:  may be obtained from Prof Simon Godsill, sjg@eng.cam.ac.uk.

To apply:  div-f@eng.cam.ac.uk. (Tel +44 01223 332752)

Please quote reference NA01469 on your application and in any correspondence about this vacancy.

The University values diversity and is committed to equality of opportunity.

The University has a responsibility to ensure that all employees are eligible to live and work in the UK.

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