Fully-funded PhD studentship in Statistical Regularisation at University College London, UK

Fully-funded PhD studentship in Statistical Regularisation at University College London, UK

Duration of studentship: 3 years
Stipend: £15,863 per annum
Studentship start date: 28th September 2015 or shortly thereafter

Application closing date: Applications will be considered on a rolling basis until the studentship is filled. Apply as soon as possible to avoid disappointment!

Applications are invited for a fully funded PhD studentship in "Statistical regularisation" at UCL. The studentship will commence 28th September 2015 or shortly thereafter, will be based in UCL's Department of Statistical Science, and will involve an extended visit (approx one
year) to the Japanese Advanced Institute of Science and Technology (JAIST). The award is tenable for 36 months and covers tuition fees plus a stipend of £15,863 per annum (based on the standard UK Research Council rate with London weighting).

This studentship may only be awarded to applicants liable to pay tuition fees at the UK/EU rate (i.e. it cannot be used to part-cover overseas tuition fees).

*Studentship Description*
The project will address the current need to progress new sparse, adaptive statistical models and approaches that have the capability to capture both the rich statistical, and structural, interdependencies present in high-dimensional image data sets. The work will draw on a combination of recent ideas that attempt to (i) accommodate both correlatory and relational interactions of heterogeneous data over, and between, multiple scales and (ii) extract, learn, and predict sparse representations of the data in an adaptive and robust manner.

The techniques of interest cross several allied research areas.
Candidates with an interest in one or more of the following are particularly encouraged to apply: regression and various manifestations of the Lasso, compressive sensing, dictionary learning and sparse coding, Gaussian processes, Markov random fields, and/or stochastic geometry.

Statistical techniques such as these continue to enjoy increasing attention in many modern, so-termed, data science problems. A key driver for this interest is the advent of recent innovations in smart technology domains such as sensors, mobile computing, and robotics where, for example, 'smart objects', unmanned vehicles, and sensor networks are set to effect significant and long-lasting impact on a multitude of sectors. Owing to the abundance of data captured from these persistent, always-on, next-generation systems there is an urgent and growing demand for statistically well-principled data analysis and signal/image processing.

It is against this general backdrop that the candidate will be afforded bountiful scope and motivation to develop interesting, and hitherto unexplored, statistical models and methodology. There will, furthermore, also exist ample opportunities to work alongside, and interact with, the immediate research group--- a focused team of other PhD students and post-docs, from myriad backgrounds, with various projects and interests in this space, at UCL, JAIST, and collaborators at Cambridge University, Intel, and beyond.

*Person Specification*
The requirement for admission to the MPhil/PhD in Statistical Science is a 1st class or high upper 2nd class BSc degree, or an MSc with merit or distinction in Mathematics, Statistics, Computer Science, Engineering, or a related quantitative discipline. Overseas qualifications of an equivalent standard are also acceptable.

Informal enquiries to Dr James Nelson
are welcomed

*How to apply*
Candidates should apply for the Research Degree: Statistical Science
(RRDSTASING01) in the usual way by completing the online form at:
and, to help notify us of potential candidates early, send a covering letter/email directly to Dr. James Nelson