- Fully Funded Phd Studentship In Statistical Classification

Tech
2013-12-07 02:00:28

Fully-funded PhD Studentship in Statistical Classification

Deadline

Value of Scholarship

Level of Study

Country Tenable

Renewable

Start Date: From January 2014

Studentship Information
Applications are invited for a PhD funding opportunity based in the UCL Department of Statistical Science. The studentship is partly funded by a Global Research Outreach award held by Dr Jinghao Xue in collaboration with the Samsung Advanced Institute of Technology.

The start date is flexible, between January and April 2014, although an early starting date is preferred.

Classification of Unbalanced Data (Imbalance Learning)

In practice we often need to classify data into two groups of extremely unbalanced sizes. This project will consider statistical techniques, such as feature selection, model selection, dimension reduction and ensemble methods, to develop a framework for robust, accurate and fast classification of class-unbalanced data. The first year of this project will involve working on data provided by Samsung.

Candidates with an interest in one or more of the following areas are strongly encouraged to apply: statistics, pattern recognition, machine learning and data mining. Informal enquiries to Dr Jinghao Xue are welcomed.

Eligibility
The award is tenable for 36 months and covers tuition fees (up to the overseas rate) plus a stipend of Â£15,726 per annum (based on the standard UK Research Council rate with London weighting).

The requirement for admission to the MPhil/PhD in Statistical Science is a 1st class or high upper 2nd class Bachelorâs degree, or a Masterâs degree with merit or distinction in Mathematics, Statistics, Computer Science, or a related quantitative discipline. Overseas qualifications of an equivalent standard are also acceptable. Further details can be found on the Departmental website.

How to Apply
For details on how to apply, please visit theÂ Apply button below.

Subject Area:Â Mathematics and Statistics

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